System and method for using ai/ml and telemedicine for invasive surgical treatment to determine a cardiac treatment plan that uses an electromechanical machine

ABSTRACT

A computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue, enable the user to perform an exercise at different exertion levels. Next, while the user uses the electromechanical machine to perform the first treatment plan, receiving, at the computing device, data from sensors configured to measure the data associated with the invasive surgical-related health issue and transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, and the invasive surgical-related health issue. The method additionally includes receiving the second treatment plan.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. Pat. Application Serial No. 17/736,891, filed May 4, 2022, titled “Systems and Methods for Using Artificial Intelligence to Implement a Cardio Protocol via a Relay-Based System,” which is a continuation-in-part of U.S. Pat. Application Serial No. 17/379,542, filed Jul. 19, 2021, titled “System and Method for Using Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines Capable of Enabling Remote Rehabilitative Compliance,” which is a continuation of U.S. Pat. Application Serial No. 17/146,705, filed Jan. 12, 2021, titled “System and Method for Using Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines Capable of Enabling Remote Rehabilitative Compliance,” which is a continuation-in-part of U.S. Pat. Application Serial No. 17/021,895, filed Sep. 15, 2020, titled “Telemedicine for Orthopedic Treatment,” which claims priority to and the benefit of U.S. Provisional Pat. Application Serial No. 62/910,232, filed Oct. 3, 2019, titled “Telemedicine for Orthopedic Treatment,” the entire disclosures of which are hereby incorporated by reference for all purposes. The application U.S. Pat. Application Serial No. 17/146,705 also claims priority to and the benefit of U.S. Provisional Pat. Application Serial No. 63/113,484, filed Nov. 13, 2020, titled “System and Method for Use of Artificial Intelligence in Telemedicine-Enabled Hardware to Optimize Rehabilitative Routines for Enabling Remote Rehabilitative Compliance,” the entire disclosures of which are hereby incorporated by reference for all purposes. This application also claims priority to and the benefit of U.S. Provisional Pat. Application Serial No. 63/407,049 filed Sep. 15, 2022, titled “Systems and Methods for Using Artificial Intelligence and an Electromechanical Machine to Aid Rehabilitation in Various Patient Markets,” the entire disclosure of which is hereby incorporated by reference for all purposes.

BACKGROUND

Remote medical assistance, also referred to, inter alia, as remote medicine, telemedicine, telemed, telmed, tel-med, or telehealth, is an at least two-way communication between a healthcare professional or providers, such as a physician or a physical therapist, and a patient using audio and/or audiovisual and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation) communications (e.g., via a computer, a smartphone, or a tablet). Telemedicine may aid a patient in performing various aspects of a rehabilitation regimen for a body part. The patient may use a patient interface in communication with an assistant interface for receiving the remote medical assistance via audio, visual, audiovisual, or other communications described elsewhere herein. Any reference herein to any particular sensorial modality shall be understood to include and to disclose by implication a different one or more sensory modalities.

Telemedicine is an option for healthcare professionals to communicate with patients and provide patient care when the patients do not want to or cannot easily go to the healthcare professionals’ offices. Telemedicine, however, has substantive limitations as the healthcare professionals cannot conduct physical examinations of the patients. Rather, the healthcare professionals must rely on verbal communication and/or limited remote observation of the patients.

Cardiovascular health refers to the health of the heart and blood vessels of an individual. Cardiovascular diseases or cardiovascular health issues include a group of diseases of the heart and blood vessels, including coronary heart disease, stroke, heart failure, heart arrhythmias, and heart valve problems. It is generally known that exercise and a healthy diet can improve cardiovascular health and reduce the chance or impact of cardiovascular disease.

Various other markets are related to health conditions associated with other portions and/or systems of a human body. For example, other prevalent health conditions pertain to pulmonary health, bariatric health, oncologic health, prostate health, and the like. There is a large portion of the population who are affected by one or more of these health conditions. Treatment and/or rehabilitation for the health conditions, as currently provided, is not adequate to satisfy the massive demand prevalent in the population worldwide.

SUMMARY

In one embodiment, a computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels. Next, while the user uses an electromechanical machine to perform the first treatment plan for the user, receiving, at the computing device, data from one or more sensors configured to measure the data associated with the invasive surgical-related health issue of the user. The electromechanical machine is configured to be used by the user while performing the first treatment plan. The method also includes transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure of perceived exertion, the data, and the invasive surgical-related health issue of the user. The method additionally includes receiving the second treatment plan.

In another embodiment, a computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to prepare a user for a planned invasive surgery. The user has a pre-surgery health before the planned invasive surgery and the first treatment plan comprises at least two exercise sessions that, based on the pre-surgery health of the user, enable the user to perform an exercise at different exertion levels. Then, while the user uses an electromechanical machine to perform the first treatment plan for the user, receiving, at the computing device, data from one or more sensors configured to measure the data associated with the pre-surgery health of the user before the planned invasive surgery. The electromechanical machine is configured to be used by the user while performing the first treatment plan. The method continues by transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, the planned invasive surgery, and the pre-surgery health of the user. The method also includes receiving the second treatment plan.

Another aspect of the disclosed embodiments includes a system that includes a processing device and a memory communicatively coupled to the processing device and capable of storing instructions. The processing device executes the instructions to perform any of the methods, operations, or steps described herein.

Another aspect of the disclosed embodiments includes a tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to perform any of the methods, operations, or steps disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity.

For a detailed description of example embodiments, reference will now be made to the accompanying drawings in which:

FIG. 1 generally illustrates a block diagram of an embodiment of a computer implemented system for managing a treatment plan according to the principles of the present disclosure;

FIG. 2 generally illustrates a perspective view of an embodiment of a treatment apparatus according to the principles of the present disclosure;

FIG. 3 generally illustrates a perspective view of a pedal of the treatment apparatus of FIG. 2 according to the principles of the present disclosure;

FIG. 4 generally illustrates a perspective view of a person using the treatment apparatus of FIG. 2 according to the principles of the present disclosure;

FIG. 5 generally illustrates an example embodiment of an overview display of an assistant interface according to the principles of the present disclosure;

FIG. 6 generally illustrates an example block diagram of training a machine learning model to output, based on data pertaining to the patient, a treatment plan for the patient according to the principles of the present disclosure;

FIG. 7 generally illustrates an embodiment of an overview display of the assistant interface presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the principles of the present disclosure;

FIG. 8 generally illustrates an example embodiment of a method for optimizing a treatment plan for a user to increase a probability of the user complying with the treatment plan according to the principles of the present disclosure;

FIG. 9 generally illustrates an example embodiment of a method for generating a treatment plan based on a desired benefit, a desired pain level, an indication of probability of complying with a particular exercise regimen, or some combination thereof according to the principles of the present disclosure;

FIG. 10 generally illustrates an example embodiment of a method for controlling, based on a treatment plan, a treatment apparatus while a user uses the treatment apparatus according to the principles of the present disclosure;

FIG. 11 generally illustrates an example computer system according to the principles of the present disclosure;

FIG. 12 generally illustrates a perspective view of a person using the treatment apparatus of FIG. 2 , the patient interface 50, and a computing device according to the principles of the present disclosure;

FIG. 13 generally illustrates a display of the computing device presenting a treatment plan designed to improve the user’s cardiovascular health according to the principles of the present disclosure;

FIG. 14 generally illustrates an example embodiment of a method for generating treatment plans including sessions designed to enable a user to achieve a desired exertion level based on a standardized measure of perceived exertion according to the principles of the present disclosure;

FIG. 15 generally illustrates an example embodiment of a method for receiving input from a user and transmitting the feedback to be used to generate a new treatment plan according to the principles of the present disclosure;

FIG. 16 generally illustrates an example embodiment of a method for implementing a cardiac rehabilitation protocol by using artificial intelligence and a standardized measurement according to the principles of the present disclosure;

FIG. 17 generally illustrates an example embodiment of a method for using artificial intelligence and machine learning and telemedicine for invasive surgical-related treatment to determine a treatment plan that uses an electromechanical machine according to the principles of the present disclosure;

FIG. 18 generally illustrates an embodiment of an enhanced patient display of the patient interface presenting real-time measurement information during a telemedicine session according to the principles of the present disclosure; and

FIG. 19 generally illustrates an example embodiment of a method for using artificial intelligence, machine learning and telemedicine for prehabilitation to determine a treatment plan that uses an electromechanical machine according to the principles of the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components. Different companies may refer to a component by different names - this document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to....” Also, the term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections.

The terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections; however, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer, or section from another region, layer, or section. Terms such as “first,” “second,” and other numerical terms, when used herein, do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer, or section discussed below could be termed a second element, component, region, layer, or section without departing from the teachings of the example embodiments. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C. In another example, the phrase “one or more” when used with a list of items means there may be one item or any suitable number of items exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,” “lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,” “contained within,” “superimposing upon,” and the like, may be used herein. These spatially relative terms can be used for ease of description to describe one element’s or feature’s relationship to another element(s) or feature(s) as illustrated in the figures. The spatially relative terms may also be intended to encompass different orientations of the device in use, or operation, in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below” or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, the example term “below” can encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptions used herein interpreted accordingly.

A “treatment plan” may include one or more treatment protocols or exercise regimens, and each treatment protocol or exercise regimen may include one or more treatment sessions or one or more exercise sessions. Each treatment session or exercise session may comprise one or more session periods or exercise periods, where each session period or exercise period may include at least one exercise for treating the body part of the patient. In some embodiments, exercises that improve the cardiovascular health of the user are included in each session. For each session, exercises may be selected to enable the user to perform at different exertion levels. The exertion level for each session may be based at least on a cardiovascular health issue of the user and/or a standardized measure comprising a degree, characterization or other quantitative or qualitative description of exertion. The cardiovascular health issues may include, without limitation, heart surgery performed on the user, a heart transplant performed on the user, a heart arrhythmia of the user, an atrial fibrillation of the user, tachycardia, bradycardia, supraventricular tachycardia, congestive heart failure, heart valve disease, arteriosclerosis, atherosclerosis, pericardial disease, pericarditis, myocardial disease, myocarditis, cardiomyopathy, congenital heart disease, or some combination thereof. The cardiovascular health issues may also include, without limitation, diagnoses, diagnostic codes, symptoms, life consequences, comorbidities, risk factors to health, life, etc. The exertion levels may progressively increase between each session. For example, an exertion level may be low for a first session, medium for a second session, and high for a third session. The exertion levels may change dynamically during performance of a treatment plan based on at least cardiovascular data received from one or more sensors, the cardiovascular health issue, and/or the standardized measure comprising a degree, characterization or other quantitative or qualitative description of exertion. Any suitable exercise (e.g., muscular, weight lifting, cardiovascular, therapeutic, neuromuscular, neurocognitive, meditating, yoga, stretching, etc.) may be included in a session period or an exercise period. For example, a treatment plan for postoperative rehabilitation after a knee surgery may include an initial treatment protocol or exercise regimen with twice daily stretching sessions for the first 3 days after surgery and a more intensive treatment protocol with active exercise sessions performed 4 times per day starting 4 days after surgery. A treatment plan may also include information pertaining to a medical procedure to perform on the patient, a treatment protocol for the patient using a treatment apparatus, a diet regimen for the patient, a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof.

The terms telemedicine, telehealth, telemed, teletherapeutic, telemedicine, remote medicine, etc. may be used interchangeably herein.

The term “optimal treatment plan” may refer to optimizing a treatment plan based on a certain parameter or factors or combinations of more than one parameter or factor, such as, but not limited to, a measure of benefit which one or more exercise regimens provide to users, one or more probabilities of users complying with one or more exercise regimens, an amount, quality or other measure of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, an indication of an energy level of the user, information pertaining to a microbiome from one or more locations on or in the user (e.g., skin, scalp, digestive tract, vascular system, etc.), or some combination thereof.

As used herein, the term healthcare professional may include a medical professional (e.g., such as a doctor, a nurse, a therapist, and the like), an exercise professional (e.g., such as a coach, a trainer, a nutritionist, and the like), or another professional sharing at least one of medical and exercise attributes (e.g., such as an exercise physiologist, a physical therapist, an occupational therapist, and the like). As used herein, and without limiting the foregoing, a “healthcare professional” may be a human being, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or an artificially intelligent entity, such entity including a software program, integrated software and hardware, or hardware alone.

Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will preferably but not determinatively be less than 10 seconds but greater than 2 seconds.

Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Rehabilitation may be directed at cardiac rehabilitation, rehabilitation from stroke, multiple sclerosis, Parkinson’s disease, myasthenia gravis, Alzheimer’s disease, any other neurodegenative or neuromuscular disease, a brain injury, a spinal cord injury, a spinal cord disease, a joint injury, a joint disease, post-surgical recovery, or the like. Rehabilitation can further involve muscular contraction in order to improve blood flow and lymphatic flow, engage the brain and nervous system to control and affect a traumatized area to increase the speed of healing, reverse or reduce pain (including arthralgias and myalgias), reverse or reduce stiffness, recover range of motion, encourage cardiovascular engagement to stimulate the release of pain-blocking hormones or to encourage highly oxygenated blood flow to aid in an overall feeling of well-being. Rehabilitation may be provided for individuals of average weight in reasonably good physical condition having no substantial deformities, as well as for individuals more typically in need of rehabilitation, such as those who are elderly, obese, subject to disease processes, injured and/or who have a severely limited range of motion. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as “pre-habilitation” or “prehab”). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre-treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient’s body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through, dissecting and/or harming numerous muscles and muscle groups in or about, without limitation, the skull or face, the abdomen, the ribs and/or the thoracic cavity, as well as in or about all joints and appendages. Prehabilitation can improve a patient’s speed of recovery, measure of quality of life, level of pain, etc. in all the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. Performance of the one or more sets of exercises may be required in order to qualify for an elective surgery, such as a knee replacement. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing muscle memory, reducing pain, reducing stiffness, establishing new muscle memory, enhancing mobility (i.e., improve range of motion), improving blood flow, and/or the like.

The phrase, and all permutations of the phrase, “respective measure of benefit with which one or more exercise regimens may provide the user” (e.g., “measure of benefit,” “respective measures of benefit,” “measures of benefit,” “measure of exercise regimen benefit,” “exercise regimen benefit measurement,” etc.) may refer to one or more measures of benefit with which one or more exercise regimens may provide the user.

“Cardiac conditions,” as used herein, may refer to medical, health or other characteristics or attributes associated with a cardiological or cardiovascular state. Cardiac conditions are descriptions, measurements, diagnoses, etc. which refer or relate to a state, attribute or explanation of a state pertaining to the cardiovascular system. For example, if one’s heart is beating too fast for a given context, then the cardiac condition describing that is “tachycardia”; if one has had the left mitral valve of the heart replaced, then the cardiac condition is that of having a replaced mitral valve. If one has suffered a myocardial infarction, that term, too, is descriptive of a cardiac condition. A distinguishing point is that a cardiac condition reflects a state of a patient’s cardiovascular system at a given point in time. It is, however, not an event or occurrence itself. Much as a needle can prick a balloon and burst the balloon, deflating it, the state or condition of the balloon is that it has been burst, while the event which caused that is entirely different, i.e., the needle pricking the balloon. Without limiting the foregoing, a cardiac condition may refer to an already existing cardiac condition, a change in state (e.g., an exacerbation or worsening) in or to an existing cardiac condition, and/or an appearance of a new cardiac condition.

A “cardiac event,” “cardiac-related event” or “CRE,” as used herein, is something that has occurred with respect to one’s cardiovascular system and it may be a contributing, associated or precipitating cause of one or more cardiac conditions, but it is the causative reason for the one or more cardiac conditions or a contributing or associated reason for the one or more cardiac conditions. For example, if an angioplasty procedure results in a rupture of a blood vessel in the heart, the rupture is the CRE, while the underlying condition that caused the angioplasty to fail was the cardiac condition of having an aneurysm. The aneurysm is a cardiac condition, not a CRE. The rupture is the CRE. The angioplasty is the cause of the CRE (the rupture), but is not a cardiac condition (a heart cannot be “angioplastic”). The angioplasty procedure can also be deemed a CRE in and of itself, because it is an active, dynamic process, not a description of a state. One or more cardiac conditions of a user may be used to describe the cardiac health of the user.

For example, and without limiting the foregoing, CREs may include cardiac-related medical conditions and events, and may also be a consequence of procedures or interventions (including, without limitation, cardiac interventions, as defined infra) that may negatively affect the health, performance, or predicted future performance of the cardiovascular system or of any physiological systems or health-related attributes of a patient where such systems or attributes are themselves affected by the performance of the patient’s cardiovascular system. These CREs may render individuals, optionally with extant comorbidities, susceptible to a first comorbidity or additional comorbidities or independent medical problems such as, without limitation, congestive heart failure, fatigue issues, oxygenation issues, pulmonary issues, vascular issues, cardio-renal anemia syndrome (CRAS), muscle loss issues, endurance issues, strength issues, sexual performance issues (such as erectile dysfunction), ambulatory issues, obesity issues, reduction of lifespan issues, reduction of quality-of-life issues, and the like. “Issues,” as used in the foregoing, may refer, without limitation, to exacerbations, reductions, mitigations, compromised functionings, eliminations, or other directly or indirectly caused changes in an underlying condition or physiological organ or psychological characteristic of the individual or the sequelae of any such change, where the existence of at least one said issue may result in a diminution of the quality of life for the individual. The existence of such an at least one issue may itself be remediated by reversing, mitigating, controlling, or otherwise ameliorating the effects of said exacerbations, reductions, mitigations, compromised functionings, eliminations, or other directly or indirectly caused changes in an underlying condition or physiological organ or psychological characteristic of the individual or the sequelae of such change. In general, when an individual suffers a CRE, the individual’s overall quality of life may become substantially degraded, compared to its prior state.

A “cardiac intervention,” as used herein, is a process, procedure, surgery, drug regimen or other medical intervention or action undertaken with the intent to minimize the negative effects of a CRE (or, if a CRE were to have positive effects, to maximize those positive effects) that has already occurred, that is about to occur or that is predicted to occur with some probability greater than zero, or to eliminate the negative effects altogether. A cardiac intervention may also be undertaken before a CRE occurs with the intent to avoid the CRE from occurring or to mitigate the negative consequences of the CRE should the CRE still occur.

A “cardiac outcome,” as used herein, is the result of either a cardiac intervention or other treatment or the result of a CRE for which no cardiac intervention or other treatment has been performed. For example, if a patient dies from the CRE of a ruptured aorta due to the cardiac condition of an aneurysm, and the death occurs because of, in spite of, or without any cardiac interventions, then the cardiac outcome is the patient’s death. On the other hand, if a patient has the cardiac conditions of atherosclerosis, hypertension, and dyspnea, and the cardiac intervention of a balloon angioplasty is performed to insert a stent to reduce the effects of arterial stenosis (another cardiac condition), then the cardiac outcome can be significantly improved cardiac health for the patient. Accordingly, a cardiac outcome may generally refer, in some examples, to both negative and positive outcomes.

To use an analogy of an automobile, an automotive condition may be dirty oil. If the oil is not changed, it may damage the engine. The engine damage is an automotive condition, but the time when the engine sustains damage due to the particulate matter in the oil is an “automotive-related event,” the analogue to a CRE. If an automotive intervention is undertaken, the oil will be changed before it can damage the engine; or, if the engine has already been damaged, then an automotive intervention involving specific repairs to the engine will be undertaken. If ultimately the engine fails to work, then the automotive outcome is a broken engine; on the other hand, if the automotive interventions succeed, then the automotive outcome is that the automobile’s performance is brought back to a factory-standard or factory-acceptable level.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of the present disclosure. Although one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment.

The following discussion is directed to various embodiments of the present disclosure. Although one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment.

Determining a treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; psychographic; geographic; diagnostic; measurement- or test-based; medically historic; behavioral historic; cognitive; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, microbiome related, pharmacologic and other treatment(s) recommended; arterial blood gas and/or oxygenation levels or percentages; glucose levels; blood oxygen levels; insulin levels; psychographics; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, a duration of use of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, a glucose level, arterial blood gas and/or oxygenation levels or percentages, or other biomarker, or some combination thereof. It may be desirable to process and analyze the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

Further, another technical problem may involve distally treating, via a computing apparatus during a telemedicine session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling, from the different location, the control of a treatment apparatus used by the patient at the patient’s location. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a healthcare professional may prescribe a treatment apparatus to the patient to use to perform a treatment protocol at their residence or at any mobile location or temporary domicile. A healthcare professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A healthcare professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

When the healthcare professional is located in a different location from the patient and the treatment apparatus, it may be technically challenging for the healthcare professional to monitor the patient’s actual progress (as opposed to relying on the patient’s word about their progress) in using the treatment apparatus, modify the treatment plan according to the patient’s progress, adapt the treatment apparatus to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

Additionally, or alternatively, a computer-implemented system may be used in connection with a treatment apparatus to treat the patient, for example, during a telemedicine session. For example, the treatment apparatus can be configured to be manipulated by a user while the user is performing a treatment plan. The system may include a patient interface that includes an output device configured to present telemedicine information associated with the telemedicine session. During the telemedicine session, the processing device can be configured to receive treatment data pertaining to the user. The treatment data may include one or more characteristics of the user. The processing device may be configured to determine, via one or more trained machine learning models, at least one respective measure of benefit which one or more exercise regimens provide the user. Determining the respective measure of benefit may be based on the treatment data. The processing device may be configured to determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens. The processing device may be configured to transmit the treatment plan, for example, to a computing device. The treatment plan can be generated based on the one or more probabilities and the respective measure of benefit which the one or more exercise regimens provide the user.

Accordingly, systems and methods, such as those described herein, that receive treatment data pertaining to the user of the treatment apparatus during telemedicine session, may be desirable.

In some embodiments, the systems and methods described herein may be configured to use a treatment apparatus configured to be manipulated by an individual while performing a treatment plan. The individual may include a user, patient, or other a person using the treatment apparatus to perform various exercises for prehabilitation, rehabilitation, stretch training, and the like. The systems and methods described herein may be configured to use and/or provide a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session.

In some embodiments, during an adaptive telemedicine session, the systems and methods described herein may be configured to use artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment apparatus based on the assignment. The term “adaptive telemedicine” may refer to a telemedicine session dynamically adapted based on one or more factors, criteria, parameters, characteristics, or the like. The one or more factors, criteria, parameters, characteristics, or the like may pertain to the user (e.g., heartrate, blood pressure, perspiration rate, pain level, or the like), the treatment apparatus (e.g., pressure, range of motion, speed of motor, etc.), details of the treatment plan, and so forth.

In some embodiments, numerous patients may be prescribed numerous treatment apparatuses because the numerous patients are recovering from the same medical procedure and/or suffering from the same injury. The numerous treatment apparatusus may be provided to the numerous patients. The treatment apparatuses may be used by the patients to perform treatment plans in their residences, at gyms, at rehabilitative centers, at hospitals, or at any suitable locations, including permanent or temporary domiciles.

In some embodiments, the treatment apparatuses may be communicatively coupled to a server. Characteristics of the patients, including the treatment data, may be collected before, during, and/or after the patients perform the treatment plans. For example, any or each of the personal information, the performance information, and the measurement information may be collected before, during, and/or after a patient performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment apparatus throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment apparatus may be collected before, during, and/or after the treatment plan is performed.

Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step or set of steps in the treatment plan. Such a technique may enable the determination of which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

Data may be collected from the treatment apparatuses and/or any suitable computing device (e.g., computing devices where personal information is entered, such as the interface of the computing device described herein, a clinician interface, patient interface, or the like) over time as the patients use the treatment apparatuses to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, and the results of the treatment plans. Further, the data may include characteristics of the treatment apparatus. The characteristics of the treatment apparatus may include a make (e.g., identity of entity that designed, manufactured, etc. the treatment apparatus 70) of the treatment apparatus 70, a model (e.g., model number or other identifier of the model) of the treatment apparatus 70, a year (e.g., year the treatment apparatus was manufactured) of the treatment apparatus 70, operational parameters (e.g., engine temperature during operation, a respective status of each of one or more sensors included in or associated with the treatment apparatus 70, vibration measurements of the treatment apparatus 70 in operation, measurements of static and/or dynamic forces exerted internally or externally on the treatment apparatus 70, etc.) of the treatment apparatus 70, settings (e.g., range of motion setting, speed setting, required pedal force setting, etc.) of the treatment apparatus 70, and the like. The data collected from the treatment apparatuses, computing devices, characteristics of the user, characteristics of the treatment apparatus, and the like may be collectively referred to as “treatment data” herein.

In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment apparatus for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. In some embodiments, the artificial intelligence engine may be used to identify trends and/or patterns and to define new cohorts based on achieving desired results from the treatment plans and machine learning models associated therewith may be trained to identify such trends and/or patterns and to recommend and rank the desirability of the new cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan.

As may be appreciated, the characteristics of the new patient (e.g., a new user) may change as the new patient uses the treatment apparatus to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now-changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’s being reassigned to a different cohort with a different weight criterion.

A different treatment plan may be selected for the new patient, and the treatment apparatus may be controlled, distally (e.g., which may be referred to as remotely) and based on the different treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment apparatus.

Further, the systems and methods described herein may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. “Real-time” may also refer to near real-time, which may be less than 10 seconds or any reasonably proximate difference between two different times. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. The term “medical action(s)” may refer to any suitable action performed by the healthcare professional, and such action or actions may include diagnoses, prescription of treatment plans, prescription of treatment apparatusus, and the making, composing and/or executing of appointments, telemedicine sessions, prescription of medicines, telephone calls, emails, text messages, and the like.

Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’s, and that a second treatment plan provides the second result for people with characteristics similar to the patient.

Further, the artificial intelligence engine may be trained to output treatment plans that are not optimal i.e., sub-optimal, nonstandard, or otherwise excluded (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient. In some embodiments, the artificial intelligence engine may monitor the treatment data received while the patient (e.g., the user) with, for example, high blood pressure, uses the treatment apparatus to perform an appropriate treatment plan and may modify the appropriate treatment plan to include features of an excluded treatment plan that may provide beneficial results for the patient if the treatment data indicates the patient is handling the appropriate treatment plan without aggravating, for example, the high blood pressure condition of the patient. In some embodiments, the artificial intelligence engine may modify the treatment plan if the monitored data shows the plan to be inappropriate or counterproductive for the user.

In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare professional. The healthcare professional may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment apparatus. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment apparatus.

In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing apparatus of a healthcare professional. The video may also be accompanied by audio, text and other multimedia information and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation). Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds. Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare professional may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the healthcare professional’s experience using the computing device and may encourage the healthcare professional to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the healthcare professional does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine may be configured to provide, dynamically on the fly, the treatment plans and excluded treatment plans.

In some embodiments, the treatment plan may be modified by a healthcare professional. For example, certain procedures may be added, modified or removed. In the telehealth scenario, there are certain procedures that may not be performed due to the distal nature of a healthcare professional using a computing device in a different physical location than a patient.

A technical problem may relate to the information pertaining to the patient’s medical condition being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). That is, some sources used by various healthcare professional entities may be installed on their local computing devices and, additionally and/or alternatively, may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, via interfaces exposed by APIs used by the sources, the formats used by the sources. In some embodiments, when information is received from the sources, the API may map and convert the format used by the sources to a standardized (i.e., canonical) format, language and/or encoding (“format” as used herein will be inclusive of all of these terms) used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when the artificial intelligence engine is performing any of the techniques disclosed herein. Using the information converted to a standardized format may enable a more accurate determination of the procedures to perform for the patient.

The various embodiments disclosed herein may provide a technical solution to the technical problem pertaining to the patient’s medical condition information being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). The information may be converted from the format used by the sources to the standardized format used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when performing any of the techniques disclosed herein. The standardized information may enable generating optimal treatment plans, where the generating is based on treatment plans associated with the standardized information. The optimal treatment plans may be provided in a standardized format that can be processed by various applications (e.g., telehealth) executing on various computing devices of healthcare professionals and/or patients.

A technical problem may include a challenge of generating treatment plans for users, such treatment plans comprising exercises that balance a measure of benefit which the exercise regimens provide to the user and the probability the user complies with the exercises (or the distinct probabilities the user complies with each of the one or more exercises). By selecting exercises having higher compliance probabilities for the user, more efficient treatment plans may be generated, and these may enable less frequent use of the treatment apparatus and therefore extend the lifetime or time between recommended maintenance of or needed repairs to the treatment apparatus. For example, if the user consistently quits a certain exercise but yet attempts to perform the exercise multiple times thereafter, the treatment apparatus may be used more times, and therefore suffer more “wear-and-tear” than if the user fully complies with the exercise regimen the first time. In some embodiments, a technical solution may include using trained machine learning models to generate treatment plans based on the measure of benefit exercise regimens provide users and the probabilities of the users associated with complying with the exercise regimens, such inclusion thereby leading to more time-efficient, cost-efficient, and maintenance-efficient use of the treatment apparatus.

In some embodiments, the treatment apparatus may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a healthcare professional may adapt, remotely during a telemedicine session, the treatment apparatus to the needs of the patient by causing a control instruction to be transmitted from a server to treatment apparatus. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis.

Center-based rehabilitation may be prescribed for certain patients that qualify and/or are eligible for cardiac rehabilitation. Further, the use of exercise equipment to stimulate blood flow and heart health may be beneficial for a plethora of other rehabilitation, in addition to cardiac rehabilitation, such as pulmonary rehabilitation, bariatric rehabilitation, cardio-oncologic rehabilitation, orthopedic rehabilitation, any other type of rehabilitation. However, center-based rehabilitation suffers from many disadvantages. For example, center-based access requires the patient to travel from their place of residence to the center to use the rehabilitation equipment. Traveling is a barrier to entry for some because not all people have vehicles or desire to spend money on gas to travel to a center. Further, center-based rehabilitation programs may not be individually tailored to a patient. That is, the center-based rehabilitation program may be one-size fits all based on a type of medical condition the patient underwent. In addition, center-based rehabilitation require the patient to adhere to a schedule of when the center is open, when the rehabilitation equipment is available, when the support staff is available, etc. In addition, center-based rehabilitation, due to the fact the rehabilitation is performed in a public center, lacks privacy. Center-based rehabilitation also suffers from weather constraints in that detrimental weather may prevent a patient from traveling to the center to comply with their rehabilitation program.

Accordingly, home-based rehabilitation may solve one or more of the issues related to center-based rehabilitation and provide various advantages over center-based rehabilitation. For example, home-based rehabilitation may require decreased days to enrollment, provide greater access for patients to engage in the rehabilitation, and provide individually tailored treatment plans based on one or more characteristics of the patient. Further, home-based rehabilitation provides greater flexibility in scheduling, as the rehabilitation may be performed at any time during the day when the user is at home and desires to perform the treatment plan. There is no transportation barrier for home-based rehabilitation since the treatment apparatus is located within the user’s residence. Home-based rehabilitation provides greater privacy for the patient because the patient is performing the treatment plan within their own residence. To that end, the treatment plan implementing the rehabilitation may be easily integrated in to the patient’s home routine. The home-based rehabilitation may be provided to more patients than center-based rehabilitation because the treatment apparatus may be delivered to rural regions. Additionally, home-based rehabilitation does not suffer from weather concerns.

FIG. 1 shows a block diagram of a computer-implemented system 10, hereinafter called “the system” for managing a treatment plan. Managing the treatment plan may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to a patient.

The system 10 also includes a server 30 configured to store and to provide data related to managing the treatment plan. The server 30 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 30 also includes a first communication interface 32 configured to communicate with the clinician interface 20 via a first network 34. In some embodiments, the first network 34 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. The server 30 includes a first processor 36 and a first machine-readable storage memory 38, which may be called a “memory” for short, holding first instructions 40 for performing the various actions of the server 30 for execution by the first processor 36. The server 30 is configured to store data regarding the treatment plan. For example, the memory 38 includes a system data store 42 configured to hold system data, such as data pertaining to treatment plans for treating one or more patients.

The system data store 42 may be configured to store optimal treatment plans generated based on one or more probabilities of users associated with complying with the exercise regimens, and the measure of benefit with which one or more exercise regimens provide the user. The system data store 42 may hold data pertaining to one or more exercises (e.g., a type of exercise, which body part the exercise affects, a duration of the exercise, which treatment apparatus to use to perform the exercise, repetitions of the exercise to perform, etc.). When any of the techniques described herein are being performed, or prior to or thereafter such performance, any of the data stored in the system data store 42 may be accessed by an artificial intelligence engine 11.

The server 30 may also be configured to store data regarding performance by a patient in following a treatment plan. For example, the memory 38 includes a patient data store 44 configured to hold patient data, such as data pertaining to the one or more patients, including data representing each patient’s performance within the treatment plan. The patient data store 44 may hold treatment data pertaining to users over time, such that historical treatment data is accumulated in the patient data store 44. The patient data store 44 may hold data pertaining to measures of benefit one or more exercises provide to users, probabilities of the users complying with the exercise regimens, and the like. The exercise regimens may include any suitable number of exercises (e.g., shoulder raises, squats, cardiovascular exercises, sit-ups, curls, etc.) to be performed by the user. When any of the techniques described herein are being performed, or prior to or thereafter such performance, any of the data stored in the patient data store 44 may be accessed by an artificial intelligence engine 11.

In addition, the determination or identification of: the characteristics (e.g., personal, performance, measurement, etc.) of the users, the treatment plans followed by the users, the measure of benefits which exercise regimens provide to the users, the probabilities of the users associated with complying with exercise regimens, the level of compliance with the treatment plans (e.g., the user completed 4 out of 5 exercises in the treatment plans, the user completed 80% of an exercise in the treatment plan, etc.), and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different patient cohort-equivalent databases in the patient data store 44. For example, the data for a first cohort of first patients having a first determined measure of benefit provided by exercise regimens, a first determined probability of the user associated with complying with exercise regimens, a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment plan followed by the first patient, and/or a first result of the treatment plan, may be stored in a first patient database. The data for a second cohort of second patients having a second determined measure of benefit provided by exercise regimens, a second determined probability of the user associated with complying with exercise regimens, a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, and/or a second result of the treatment plan may be stored in a second patient database. Any single characteristic, any combination of characteristics, or any measures calculation therefrom or thereupon may be used to separate the patients into cohorts. In some embodiments, the different cohorts of patients may be stored in different partitions or volumes of the same database. There is no specific limit to the number of different cohorts of patients allowed, other than as limited by mathematical combinatoric and/or partition theory.

This measure of exercise benefit data, user compliance probability data, characteristic data, treatment plan data, and results data may be obtained from numerous treatment apparatuses and/or computing devices over time and stored in the database 44. The measure of exercise benefit data, user compliance probability data, characteristic data, treatment plan data, and results data may be correlated in the patient-cohort databases in the patient data store 44. The characteristics of the users may include personal information, performance information, and/or measurement information.

In addition to the historical treatment data, measure of exercise benefit data, and/or user compliance probability data about other users stored in the patient cohort-equivalent databases, real-time or near-real-time information based on the current patient’s treatment data, measure of exercise benefit data, and/or user compliance probability data about a current patient being treated may be stored in an appropriate patient cohort-equivalent database. The treatment data, measure of exercise benefit data, and/or user compliance probability data of the patient may be determined to match or be similar to the treatment data, measure of exercise benefit data, and/or user compliance probability data of another person in a particular cohort (e.g., a first cohort “A”, a second cohort “B” or a third cohort “C”, etc.) and the patient may be assigned to the selected or associated cohort.

In some embodiments, the server 30 may execute the artificial intelligence (AI) engine 11 that uses one or more machine learning models 13 to perform at least one of the embodiments disclosed herein. The server 30 may include a training engine 9 capable of generating the one or more machine learning models 13. The machine learning models 13 may be trained to assign users to certain cohorts based on their treatment data, generate treatment plans using real-time and historical data correlations involving patient cohort-equivalents, and control a treatment apparatus 70, among other things. The machine learning models 13 may be trained to generate, based on one or more probabilities of the user complying with one or more exercise regimens and/or a respective measure of benefit one or more exercise regimens provide the user, a treatment plan at least a subset of the one or more exercises for the user to perform. The one or more machine learning models 13 may be generated by the training engine 9 and may be implemented in computer instructions executable by one or more processing devices of the training engine 9 and/or the servers 30. To generate the one or more machine learning models 13, the training engine 9 may train the one or more machine learning models 13. The one or more machine learning models 13 may be used by the artificial intelligence engine 11.

The training engine 9 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) device, any other desired computing device, or any combination of the above. The training engine 9 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine 9 may use a training data set of a corpus of information (e.g., treatment data, measures of benefits of exercises provide to users, probabilities of users complying with the one or more exercise regimens, etc.) pertaining to users who performed treatment plans using the treatment apparatus 70, the details (e.g., treatment protocol including exercises, amount of time to perform the exercises, instructions for the patient to follow, how often to perform the exercises, a schedule of exercises, parameters/configurations/settings of the treatment apparatus 70 throughout each step of the treatment plan, etc.) of the treatment plans performed by the users using the treatment apparatus 70, and/or the results of the treatment plans performed by the users, etc.

The one or more machine learning models 13 may be trained to match patterns of treatment data of a user with treatment data of other users assigned to a particular cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, a probabilistic match, etc. The one or more machine learning models 13 may be trained to receive the treatment data of a patient as input, map the treatment data to the treatment data of users assigned to a cohort, and determine a respective measure of benefit one or more exercise regimens provide to the user based on the measures of benefit the exercises provided to the users assigned to the cohort. The one or more machine learning models 13 may be trained to receive the treatment data of a patient as input, map the treatment data to treatment data of users assigned to a cohort, and determine one or more probabilities of the user associated with complying with the one or more exercise regimens based on the probabilities of the users in the cohort associated with complying with the one or more exercise regimens. The one or more machine learning models 13 may also be trained to receive various input (e.g., the respective measure of benefit which one or more exercise regimens provide the user; the one or more probabilities of the user complying with the one or more exercise regimens; an amount, quality or other measure of sleep associated with the user; information pertaining to a diet of the user, information pertaining to an eating schedule of the user; information pertaining to an age of the user, information pertaining to a sex of the user; information pertaining to a gender of the user; an indication of a mental state of the user; information pertaining to a genetic condition of the user; information pertaining to a disease state of the user; an indication of an energy level of the user; or some combination thereof), and to output a generated treatment plan for the patient.

The one or more machine learning models 13 may be trained to match patterns of a first set of parameters (e.g., treatment data, measures of benefits of exercises provided to users, probabilities of user compliance associated with the exercises, etc.) with a second set of parameters associated with an optimal treatment plan. The one or more machine learning models 13 may be trained to receive the first set of parameters as input, map the characteristics to the second set of parameters associated with the optimal treatment plan, and select the optimal treatment plan. The one or more machine learning models 13 may also be trained to control, based on the treatment plan, the treatment apparatus 70.

Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 13 may refer to model artifacts created by the training engine 9. The training engine 9 may find patterns in the training data wherein such patterns map the training input to the target output, and generate the machine learning models 13 that capture these patterns. In some embodiments, the artificial intelligence engine 11, the database 33, and/or the training engine 9 may reside on another component (e.g., assistant interface 94, clinician interface 20, etc.) depicted in FIG. 1 .

The one or more machine learning models 13 may comprise, e.g., a single level of linear or non-linear operations (e.g., a support vector machine [SVM]) or the machine learning models 13 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Examples of deep networks are neural networks including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (e.g., each neuron may transmit its output signal to the input of the remaining neurons, as well as to itself). For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

Further, in some embodiments, based on subsequent data (e.g., treatment data, measures of exercise benefit data, probabilities of user compliance data, treatment plan result data, etc.) received, the machine learning models 13 may be continuously or continually updated. For example, the machine learning models 13 may include one or more hidden layers, weights, nodes, parameters, and the like. As the subsequent data is received, the machine learning models 13 may be updated such that the one or more hidden layers, weights, nodes, parameters, and the like are updated to match or be computable from patterns found in the subsequent data. Accordingly, the machine learning models 13 may be re-trained on the fly as subsequent data is received, and therefore, the machine learning models 13 may continue to learn.

The system 10 also includes a patient interface 50 configured to communicate information to a patient and to receive feedback from the patient. Specifically, the patient interface includes an input device 52 and an output device 54, which may be collectively called a patient user interface 52, 54. The input device 52 may include one or more devices, such as a keyboard, a mouse, a touch screen input, a gesture sensor, and/or a microphone and processor configured for voice recognition. The output device 54 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, smartphone, or a smart watch. The output device 54 may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc. The output device 54 may incorporate various different visual, audio, or other presentation technologies. For example, the output device 54 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, and/or melodies, which may signal different conditions and/or directions and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation) communication devices. The output device 54 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the patient. The output device 54 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.). In some embodiments, the patient interface 50 may include functionality provided by or similar to existing voice-based assistants such as Siri by Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung.

In some embodiments, the output device 54 may present a user interface that may present a recommended treatment plan, excluded treatment plan, or the like to the patient. The user interface may include one or more graphical elements that enable the user to select which treatment plan to perform. Responsive to receiving a selection of a graphical element (e.g., “Start” button) associated with a treatment plan via the input device 54, the patient interface 50 may communicate a control signal to the controller 72 of the treatment apparatus, wherein the control signal causes the treatment apparatus 70 to begin execution of the selected treatment plan. As described below, the control signal may control, based on the selected treatment plan, the treatment apparatus 70 by causing actuation of the actuator 78 (e.g., cause a motor to drive rotation of pedals of the treatment apparatus at a certain speed), causing measurements to be obtained via the sensor 76, or the like. The patient interface 50 may communicate, via a local communication interface 68, the control signal to the treatment apparatus 70.

As shown in FIG. 1 , the patient interface 50 includes a second communication interface 56, which may also be called a remote communication interface configured to communicate with the server 30 and/or the clinician interface 20 via a second network 58. In some embodiments, the second network 58 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 58 may include the Internet, and communications between the patient interface 50 and the server 30 and/or the clinician interface 20 may be secured via encryption, such as, for example, by using a virtual private network (VPN). In some embodiments, the second network 58 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. In some embodiments, the second network 58 may be the same as and/or operationally coupled to the first network 34.

The patient interface 50 includes a second processor 60 and a second machine-readable storage memory 62 holding second instructions 64 for execution by the second processor 60 for performing various actions of patient interface 50. The second machine-readable storage memory 62 also includes a local data store 66 configured to hold data, such as data pertaining to a treatment plan and/or patient data, such as data representing a patient’s performance within a treatment plan. The patient interface 50 also includes a local communication interface 68 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 50. The local communication interface 68 may include wired and/or wireless communications. In some embodiments, the local communication interface 68 may include a local wireless network such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.

The system 10 also includes a treatment apparatus 70 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 70 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 70 may be any suitable medical, rehabilitative, therapeutic, etc. apparatus configured to be controlled distally via another computing device to treat a patient and/or exercise the patient. The treatment apparatus 70 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, a bone, or a muscle group, such as one or more vertebrae, a tendon, or a ligament. As shown in FIG. 1 , the treatment apparatus 70 includes a controller 72, which may include one or more processors, computer memory, and/or other components. The treatment apparatus 70 also includes a fourth communication interface 74 configured to communicate with the patient interface 50 via the local communication interface 68. The treatment apparatus 70 also includes one or more internal sensors 76 and an actuator 78, such as a motor. The actuator 78 may be used, for example, for moving the patient’s body part and/or for resisting forces by the patient.

The internal sensors 76 may measure one or more operating characteristics of the treatment apparatus 70 such as, for example, a force, a position, a speed, a velocity, and/or an acceleration. In some embodiments, the internal sensors 76 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a body part of the patient. For example, an internal sensor 76 in the form of a position sensor may measure a distance that the patient is able to move a part of the treatment apparatus 70, where such distance may correspond to a range of motion that the patient’s body part is able to achieve. In some embodiments, the internal sensors 76 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 76 in the form of a force sensor may measure a force or weight the patient is able to apply, using a particular body part, to the treatment apparatus 70.

The system 10 shown in FIG. 1 also includes an ambulation sensor 82, which communicates with the server 30 via the local communication interface 68 of the patient interface 50. The ambulation sensor 82 may track and store a number of steps taken by the patient. In some embodiments, the ambulation sensor 82 may take the form of a wristband, wristwatch, or smart watch. In some embodiments, the ambulation sensor 82 may be integrated within a phone, such as a smartphone.

The system 10 shown in FIG. 1 also includes a goniometer 84, which communicates with the server 30 via the local communication interface 68 of the patient interface 50. The goniometer 84 measures an angle of the patient’s body part. For example, the goniometer 84 may measure the angle of flex of a patient’s knee or elbow or shoulder.

The system 10 shown in FIG. 1 also includes a pressure sensor 86, which communicates with the server 30 via the local communication interface 68 of the patient interface 50. The pressure sensor 86 measures an amount of pressure or weight applied by a body part of the patient. For example, pressure sensor 86 may measure an amount of force applied by a patient’s foot when pedaling a stationary bike.

The system 10 shown in FIG. 1 also includes a supervisory interface 90 which may be similar or identical to the clinician interface 20. In some embodiments, the supervisory interface 90 may have enhanced functionality beyond what is provided on the clinician interface 20. The supervisory interface 90 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon.

The system 10 shown in FIG. 1 also includes a reporting interface 92 which may be similar or identical to the clinician interface 20. In some embodiments, the reporting interface 92 may have less functionality from what is provided on the clinician interface 20. For example, the reporting interface 92 may not have the ability to modify a treatment plan. Such a reporting interface 92 may be used, for example, by a biller to determine the use of the system 10 for billing purposes. In another example, the reporting interface 92 may not have the ability to display patient identifiable information, presenting only pseudonymized data and/or anonymized data for certain data fields concerning a data subject and/or for certain data fields concerning a quasi-identifier of the data subject. Such a reporting interface 92 may be used, for example, by a researcher to determine various effects of a treatment plan on different patients.

The system 10 includes an assistant interface 94 for an assistant, such as a doctor, a nurse, a physical therapist, or a technician, to remotely communicate with the patient interface 50 and/or the treatment apparatus 70. Such remote communications may enable the assistant to provide assistance or guidance to a patient using the system 10. More specifically, the assistant interface 94 is configured to communicate a telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b with the patient interface 50 via a network connection such as, for example, via the first network 34 and/or the second network 58. The telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b comprises one of an audio signal 96, an audiovisual signal 97, an interface control signal 98 a for controlling a function of the patient interface 50, an interface monitor signal 98 b for monitoring a status of the patient interface 50, an apparatus control signal 99 a for changing an operating parameter of the treatment apparatus 70, and/or an apparatus monitor signal 99 b for monitoring a status of the treatment apparatus 70. In some embodiments, each of the control signals 98 a, 99 a may be unidirectional, conveying commands from the assistant interface 94 to the patient interface 50. In some embodiments, in response to successfully receiving a control signal 98 a, 99 a and/or to communicate successful and/or unsuccessful implementation of the requested control action, an acknowledgement message may be sent from the patient interface 50 to the assistant interface 94. In some embodiments, each of the monitor signals 98 b, 99 b may be unidirectional, status-information commands from the patient interface 50 to the assistant interface 94. In some embodiments, an acknowledgement message may be sent from the assistant interface 94 to the patient interface 50 in response to successfully receiving one of the monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as a pass-through for the apparatus control signals 99 a and the apparatus monitor signals 99 b between the treatment apparatus 70 and one or more other devices, such as the assistant interface 94 and/or the server 30. For example, the patient interface 50 may be configured to transmit an apparatus control signal 99 a to the treatment apparatus 70 in response to an apparatus control signal 99 a within the telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b from the assistant interface 94. In some embodiments, the assistant interface 94 transmits the apparatus control signal 99 a (e.g., control instruction that causes an operating parameter of the treatment apparatus 70 to change) to the treatment apparatus 70 via any suitable network disclosed herein.

In some embodiments, the assistant interface 94 may be presented on a shared physical device as the clinician interface 20. For example, the clinician interface 20 may include one or more screens that implement the assistant interface 94. Alternatively or additionally, the clinician interface 20 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the output device 54 of the patient interface 50. For example, a tutorial video may be streamed from the server 30 and presented upon the patient interface 50. Content from the prerecorded source may be requested by the patient via the patient interface 50. Alternatively, via a control on the assistant interface 94, the assistant may cause content from the prerecorded source to be played on the patient interface 50.

The assistant interface 94 includes an assistant input device 22 and an assistant display 24, which may be collectively called an assistant user interface 22, 24. The assistant input device 22 may include one or more of a telephone, a keyboard, a mouse, a trackpad, or a touch screen, for example. Alternatively or additionally, the assistant input device 22 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or wide-area microphone or microphones configured for the assistant to speak to a patient via the patient interface 50. In some embodiments, assistant input device 22 may be configured to provide voice-based functionalities, with hardware and/or software configured to interpret spoken instructions by the assistant by using the one or more microphones. The assistant input device 22 may include functionality provided by or similar to existing voice-based assistants such as Siri by Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. The assistant input device 22 may include other hardware and/or software components. The assistant input device 22 may include one or more general purpose devices and/or special-purpose devices.

The assistant display 24 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, or a smart watch. The assistant display 24 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc. The assistant display 24 may incorporate various different visual, audio, or other presentation technologies. For example, the assistant display 24 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, melodies, and/or compositions, which may signal different conditions and/or directions. The assistant display 24 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the assistant. The assistant display 24 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

In some embodiments, the system 10 may provide computer translation of language from the assistant interface 94 to the patient interface 50 and/or vice-versa. The computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, the system 10 may provide voice recognition and/or spoken pronunciation of text. For example, the system 10 may convert spoken words to printed text and/or the system 10 may audibly speak language from printed text. The system 10 may be configured to recognize spoken words by any or all of the patient, the clinician, and/or the healthcare professional. In some embodiments, the system 10 may be configured to recognize and react to spoken requests or commands by the patient. For example, in response to a verbal command by the patient (which may be given in any one of several different languages), the system 10 may automatically initiate a telemedicine session.

In some embodiments, the server 30 may generate aspects of the assistant display 24 for presentation by the assistant interface 94. For example, the server 30 may include a web server configured to generate the display screens for presentation upon the assistant display 24. For example, the artificial intelligence engine 11 may generate recommended treatment plans and/or excluded treatment plans for patients and generate the display screens including those recommended treatment plans and/or external treatment plans for presentation on the assistant display 24 of the assistant interface 94. In some embodiments, the assistant display 24 may be configured to present a virtualized desktop hosted by the server 30. In some embodiments, the server 30 may be configured to communicate with the assistant interface 94 via the first network 34. In some embodiments, the first network 34 may include a local area network (LAN), such as an Ethernet network.

In some embodiments, the first network 34 may include the Internet, and communications between the server 30 and the assistant interface 94 may be secured via privacy enhancing technologies, such as, for example, by using encryption over a virtual private network (VPN). Alternatively or additionally, the server 30 may be configured to communicate with the assistant interface 94 via one or more networks independent of the first network 34 and/or other communication means, such as a direct wired or wireless communication channel. In some embodiments, the patient interface 50 and the treatment apparatus 70 may each operate from a patient location geographically separate from a location of the assistant interface 94. For example, the patient interface 50 and the treatment apparatus 70 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the assistant interface 94 at a centralized location, such as a clinic or a call center.

In some embodiments, the assistant interface 94 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or at one or more clinicians’ offices. In some embodiments, a plurality of assistant interfaces 94 may be distributed geographically. In some embodiments, a person may work as an assistant remotely from any conventional office infrastructure. Such remote work may be performed, for example, where the assistant interface 94 takes the form of a computer and/or telephone. This remote work functionality may allow for work-from-home arrangements that may include part time and/or flexible work hours for an assistant.

FIGS. 2-3 show an embodiment of a treatment apparatus 70. More specifically, FIG. 2 shows a treatment apparatus 70 in the form of a stationary cycling machine 100, which may be called a stationary bike, for short. The stationary cycling machine 100 includes a set of pedals 102 each attached to a pedal arm 104 for rotation about an axle 106. In some embodiments, and as shown in FIG. 2 , the pedals 102 are movable on the pedal arms 104 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 106 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 106. A pressure sensor 86 is attached to or embedded within one of the pedals 102 for measuring an amount of force applied by the patient on the pedal 102. The pressure sensor 86 may communicate wirelessly to the treatment apparatus 70 and/or to the patient interface 50.

FIG. 4 shows a person (a patient) using the treatment apparatus of FIG. 2 , and showing sensors and various data parameters connected to a patient interface 50. The example patient interface 50 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 50 may be embedded within or attached to the treatment apparatus 70. FIG. 4 shows the patient wearing the ambulation sensor 82 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 82 has recorded and transmitted that step count to the patient interface 50. FIG. 4 also shows the patient wearing the goniometer 84 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 84 is measuring and transmitting that knee angle to the patient interface 50. FIG. 4 also shows a right side of one of the pedals 102 with a pressure sensor 86 showing “FORCE 12.5 lbs.,” indicating that the right pedal pressure sensor 86 is measuring and transmitting that force measurement to the patient interface 50. FIG. 4 also shows a left side of one of the pedals 102 with a pressure sensor 86 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 86 is measuring and transmitting that force measurement to the patient interface 50. FIG. 4 also shows other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment apparatus 70 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 50 based on information received from the treatment apparatus 70. FIG. 4 also shows an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patent in response to a solicitation, such as a question, presented upon the patient interface 50.

FIG. 5 is an example embodiment of an overview display 120 of the assistant interface 94. Specifically, the overview display 120 presents several different controls and interfaces for the assistant to remotely assist a patient with using the patient interface 50 and/or the treatment apparatus 70. This remote assistance functionality may also be called telemedicine or telehealth.

Specifically, the overview display 120 includes a patient profile display 130 presenting biographical information regarding a patient using the treatment apparatus 70. The patient profile display 130 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 , although the patient profile display 130 may take other forms, such as a separate screen or a popup window. In some embodiments, the patient profile display 130 may include a limited subset of the patient’s biographical information. More specifically, the data presented upon the patient profile display 130 may depend upon the assistant’s need for that information. For example, a healthcare professional that is assisting the patient with a medical issue may be provided with medical history information regarding the patient, whereas a technician troubleshooting an issue with the treatment apparatus 70 may be provided with a much more limited set of information regarding the patient. The technician, for example, may be given only the patient’s name. The patient profile display 130 may include pseudonymized data and/or anonymized data or use any privacy enhancing technology to prevent confidential patient data from being communicated in a way that could violate patient confidentiality requirements. Such privacy enhancing technologies may enable compliance with laws, regulations, or other rules of governance such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA), or the General Data Protection Regulation (GDPR), wherein the patient may be deemed a “data subject”.

In some embodiments, the patient profile display 130 may present information regarding the treatment plan for the patient to follow in using the treatment apparatus 70. Such treatment plan information may be limited to an assistant who is a healthcare professional, such as a doctor or physical therapist. For example, a healthcare professional assisting the patient with an issue regarding the treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting an issue with the treatment apparatus 70 may not be provided with any information regarding the patient’s treatment plan.

In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented in the patient profile display 130 to the assistant. The one or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 11 of the server 30 and received from the server 30 in real-time during, inter alia, a telemedicine or telehealth session. An example of presenting the one or more recommended treatment plans and/or excluded treatment plans is described below with reference to FIG. 7 .

The example overview display 120 shown in FIG. 5 also includes a patient status display 134 presenting status information regarding a patient using the treatment apparatus. The patient status display 134 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 , although the patient status display 134 may take other forms, such as a separate screen or a popup window. The patient status display 134 includes sensor data 136 from one or more of the external sensors 82, 84, 86, and/or from one or more internal sensors 76 of the treatment apparatus 70. In some embodiments, the patient status display 134 may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 70. The one or more wearable devices may include a watch, a bracelet, a necklace, a chest strap, and the like. The one or more wearable devices may be configured to monitor a heartrate, a temperature, a blood pressure, one or more vital signs, and the like of the patient while the patient is using the treatment device 70. In some embodiments, the patient status display 134 may present other data 138 regarding the patient, such as last reported pain level, or progress within a treatment plan.

User access controls may be used to limit access, including what data is available to be viewed and/or modified, on any or all of the user interfaces 20, 50, 90, 92, 94 of the system 10. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 10. For example, data presented on the assistant interface 94 may be controlled by user access controls, with permissions set depending on the assistant/user’s need for and/or qualifications to view that information.

The example overview display 120 shown in FIG. 5 also includes a help data display 140 presenting information for the assistant to use in assisting the patient. The help data display 140 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 . The help data display 140 may take other forms, such as a separate screen or a popup window. The help data display 140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 50 and/or the treatment apparatus 70. The help data display 140 may also include research data or best practices. In some embodiments, the help data display 140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient’s problem. In some embodiments, the assistant interface 94 may present two or more help data displays 140, which may be the same or different, for simultaneous presentation of help data for use by the assistant. for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient’s problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.

The example overview display 120 shown in FIG. 5 also includes a patient interface control 150 presenting information regarding the patient interface 50, and/or to modify one or more settings of the patient interface 50. The patient interface control 150 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 . The patient interface control 150 may take other forms, such as a separate screen or a popup window. The patient interface control 150 may present information communicated to the assistant interface 94 via one or more of the interface monitor signals 98 b. As shown in FIG. 5 , the patient interface control 150 includes a display feed 152 of the display presented by the patient interface 50. In some embodiments, the display feed 152 may include a live copy of the display screen currently being presented to the patient by the patient interface 50. In other words, the display feed 152 may present an image of what is presented on a display screen of the patient interface 50. In some embodiments, the display feed 152 may include abbreviated information regarding the display screen currently being presented by the patient interface 50, such as a screen name or a screen number. The patient interface control 150 may include a patient interface setting control 154 for the assistant to adjust or to control one or more settings or aspects of the patient interface 50. In some embodiments, the patient interface setting control 154 may cause the assistant interface 94 to generate and/or to transmit an interface control signal 98 for controlling a function or a setting of the patient interface 50.

In some embodiments, the patient interface setting control 154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 50. For example, the patient interface setting control 154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 50 and/or to remotely control a cursor on the patient interface 50 using a mouse or touchscreen of the assistant interface 94.

In some embodiments, using the patient interface 50, the patient interface setting control 154 may allow the assistant to change a setting that cannot be changed by the patient. For example, the patient interface 50 may be precluded from accessing a language setting to prevent a patient from inadvertently switching, on the patient interface 50, the language used for the displays, whereas the patient interface setting control 154 may enable the assistant to change the language setting of the patient interface 50. In another example, the patient interface 50 may not be able to change a font size setting to a smaller size in order to prevent a patient from inadvertently switching the font size used for the displays on the patient interface 50 such that the display would become illegible to the patient, whereas the patient interface setting control 154 may provide for the assistant to change the font size setting of the patient interface 50.

The example overview display 120 shown in FIG. 5 also includes an interface communications display 156 showing the status of communications between the patient interface 50 and one or more other devices 70, 82, 84, such as the treatment apparatus 70, the ambulation sensor 82, and/or the goniometer 84. The interface communications display 156 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 . The interface communications display 156 may take other forms, such as a separate screen or a popup window. The interface communications display 156 may include controls for the assistant to remotely modify communications with one or more of the other devices 70, 82, 84. For example, the assistant may remotely command the patient interface 50 to reset communications with one of the other devices 70, 82, 84, or to establish communications with a new one of the other devices 70, 82, 84. This functionality may be used, for example, where the patient has a problem with one of the other devices 70, 82, 84, or where the patient receives a new or a replacement one of the other devices 70, 82, 84.

The example overview display 120 shown in FIG. 5 also includes an apparatus control 160 for the assistant to view and/or to control information regarding the treatment apparatus 70. The apparatus control 160 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 . The apparatus control 160 may take other forms, such as a separate screen or a popup window. The apparatus control 160 may include an apparatus status display 162 with information regarding the current status of the apparatus. The apparatus status display 162 may present information communicated to the assistant interface 94 via one or more of the apparatus monitor signals 99 b. The apparatus status display 162 may indicate whether the treatment apparatus 70 is currently communicating with the patient interface 50. The apparatus status display 162 may present other current and/or historical information regarding the status of the treatment apparatus 70.

The apparatus control 160 may include an apparatus setting control 164 for the assistant to adjust or control one or more aspects of the treatment apparatus 70. The apparatus setting control 164 may cause the assistant interface 94 to generate and/or to transmit an apparatus control signal 99 a for changing an operating parameter of the treatment apparatus 70, (e.g., a pedal radius setting, a resistance setting, a target RPM, other suitable characteristics of the treatment device 70, or a combination thereof).

The apparatus setting control 164 may include a mode button 166 and a position control 168, which may be used in conjunction for the assistant to place an actuator 78 of the treatment apparatus 70 in a manual mode, after which a setting, such as a position or a speed of the actuator 78, can be changed using the position control 168. The mode button 166 may provide for a setting, such as a position, to be toggled between automatic and manual modes. In some embodiments, one or more settings may be adjustable at any time, and without having an associated auto/manual mode. In some embodiments, the assistant may change an operating parameter of the treatment apparatus 70, such as a pedal radius setting, while the patient is actively using the treatment apparatus 70. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 50. In some embodiments, the apparatus setting control 164 may allow the assistant to change a setting that cannot be changed by the patient using the patient interface 50. For example, the patient interface 50 may be precluded from changing a preconfigured setting, such as a height or a tilt setting of the treatment apparatus 70, whereas the apparatus setting control 164 may provide for the assistant to change the height or tilt setting of the treatment apparatus 70.

The example overview display 120 shown in FIG. 5 also includes a patient communications control 170 for controlling an audio or an audiovisual communications session with the patient interface 50. The communications session with the patient interface 50 may comprise a live feed from the assistant interface 94 for presentation by the output device of the patient interface 50. The live feed may take the form of an audio feed and/or a video feed. In some embodiments, the patient interface 50 may be configured to provide two-way audio or audiovisual communications with a person using the assistant interface 94. Specifically, the communications session with the patient interface 50 may include bidirectional (two-way) video or audiovisual feeds, with each of the patient interface 50 and the assistant interface 94 presenting video of the other one. In some embodiments, the patient interface 50 may present video from the assistant interface 94, while the assistant interface 94 presents only audio or the assistant interface 94 presents no live audio or visual signal from the patient interface 50. In some embodiments, the assistant interface 94 may present video from the patient interface 50, while the patient interface 50 presents only audio or the patient interface 50 presents no live audio or visual signal from the assistant interface 94.

In some embodiments, the audio or an audiovisual communications session with the patient interface 50 may take place, at least in part, while the patient is performing the rehabilitation regimen upon the body part. The patient communications control 170 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 . The patient communications control 170 may take other forms, such as a separate screen or a popup window. The audio and/or audiovisual communications may be processed and/or directed by the assistant interface 94 and/or by another device or devices, such as a telephone system, or a videoconferencing system used by the assistant while the assistant uses the assistant interface 94. Alternatively or additionally, the audio and/or audiovisual communications may include communications with a third party. For example, the system 10 may enable the assistant to initiate a 3-way conversation regarding use of a particular piece of hardware or software, with the patient and a subject matter expert, such as a medical professional or a specialist. The example patient communications control 170 shown in FIG. 5 includes call controls 172 for the assistant to use in managing various aspects of the audio or audiovisual communications with the patient. The call controls 172 include a disconnect button 174 for the assistant to end the audio or audiovisual communications session. The call controls 172 also include a mute button 176 to temporarily silence an audio or audiovisual signal from the assistant interface 94. In some embodiments, the call controls 172 may include other features, such as a hold button (not shown). The call controls 172 also include one or more record/playback controls 178, such as record, play, and pause buttons to control, with the patient interface 50, recording and/or playback of audio and/or video from the teleconference session (e.g., which may be referred to herein as the virtual conference room). The call controls 172 also include a video feed display 180 for presenting still and/or video images from the patient interface 50, and a self-video display 182 showing the current image of the assistant using the assistant interface. The self-video display 182 may be presented as a picture-in-picture format, within a section of the video feed display 180, as shown in FIG. 5 . Alternatively or additionally, the self-video display 182 may be presented separately and/or independently from the video feed display 180.

The example overview display 120 shown in FIG. 5 also includes a third party communications control 190 for use in conducting audio and/or audiovisual communications with a third party. The third party communications control 190 may take the form of a portion or region of the overview display 120, as shown in FIG. 5 . The third party communications control 190 may take other forms, such as a display on a separate screen or a popup window. The third party communications control 190 may include one or more controls, such as a contact list and/or buttons or controls to contact a third party regarding use of a particular piece of hardware or software, e.g., a subject matter expert, such as a medical professional or a specialist. The third party communications control 190 may include conference calling capability for the third party to simultaneously communicate with both the assistant via the assistant interface 94, and with the patient via the patient interface 50. For example, the system 10 may provide for the assistant to initiate a 3-way conversation with the patient and the third party.

FIG. 6 shows an example block diagram of training a machine learning model 13 to output, based on data 600 pertaining to the patient, a treatment plan 602 for the patient according to the present disclosure. Data pertaining to other patients may be received by the server 30. The other patients may have used various treatment apparatuses to perform treatment plans. The data may include characteristics of the other patients, the details of the treatment plans performed by the other patients, and/or the results of performing the treatment plans (e.g., a percent of recovery of a portion of the patients’ bodies, an amount of recovery of a portion of the patients’ bodies, an amount of increase or decrease in muscle strength of a portion of patients’ bodies, an amount of increase or decrease in range of motion of a portion of patients’ bodies, etc.).

As depicted, the data has been assigned to different cohorts. Cohort A includes data for patients having similar first characteristics, first treatment plans, and first results. Cohort B includes data for patients having similar second characteristics, second treatment plans, and second results. For example, cohort A may include first characteristics of patients in their twenties without any medical conditions who underwent surgery for a broken limb; their treatment plans may include a certain treatment protocol (e.g., use the treatment apparatus 70 for 30 minutes 5 times a week for 3 weeks, wherein values for the properties, configurations, and/or settings of the treatment apparatus 70 are set to X (where X is a numerical value) for the first two weeks and to Y (where Y is a numerical value) for the last week).

Cohort A and cohort B may be included in a training dataset used to train the machine learning model 13. The machine learning model 13 may be trained to match a pattern between characteristics for each cohort and output the treatment plan that provides the result. Accordingly, when the data 600 for a new patient is input into the trained machine learning model 13, the trained machine learning model 13 may match the characteristics included in the data 600 with characteristics in either cohort A or cohort B and output the appropriate treatment plan 602. In some embodiments, the machine learning model 13 may be trained to output one or more excluded treatment plans that should not be performed by the new patient.

FIG. 7 shows an embodiment of an overview display 120 of the assistant interface 94 presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. As depicted, the overview display 120 only includes sections for the patient profile 130 and the video feed display 180, including the self-video display 182. Any suitable configuration of controls and interfaces of the overview display 120 described with reference to FIG. 5 may be presented in addition to or instead of the patient profile 130, the video feed display 180, and the self-video display 182.

The healthcare professional using the assistant interface 94 (e.g., computing device) during the telemedicine session may be presented in the self-video 182 in a portion of the overview display 120 (e.g., user interface presented on a display screen 24 of the assistant interface 94) that also presents a video from the patient in the video feed display 180. Further, the video feed display 180 may also include a graphical user interface (GUI) object 700 (e.g., a button) that enables the healthcare professional to share on the patient interface 50, in real-time or near real-time during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient. The healthcare professional may select the GUI object 700 to share the recommended treatment plans and/or the excluded treatment plans. As depicted, another portion of the overview display 120 includes the patient profile display 130.

In FIG. 7 , the patient profile display 130 is presenting two example recommended treatment plans 708 and one example excluded treatment plan 710. As described herein, the treatment plans may be recommended based on the one or more probabilities and the respective measure of benefit the one or more exercises provide the user. The trained machine learning models 13 may (i) use treatment data pertaining to a user to determine a respective measure of benefit which one or more exercise regimens provide the user, (ii) determine one or more probabilities of the user associated with complying with the one or more exercise regimens, and (iii) generate, using the one or more probabilities and the respective measure of benefit the one or more exercises provide to the user, the treatment plan. In some embodiments, the one or more trained machine learning models 13 may generate treatment plans including exercises associated with a certain threshold (e.g., any suitable percentage metric, value, percentage, number, indicator, probability, etc., which may be configurable) associated with the user complying with the one or more exercise regimens to enable achieving a higher user compliance with the treatment plan. In some embodiments, the one or more trained machine learning models 13 may generate treatment plans including exercises associated with a certain threshold (e.g., any suitable percentage metric, value, percentage, number, indicator, probability, etc., which may be configurable) associated with one or more measures of benefit the exercises provide to the user to enable achieving the benefits (e.g., strength, flexibility, range of motion, etc.) at a faster rate, at a greater proportion, etc. In some embodiments, when both the measures of benefit and the probability of compliance are considered by the trained machine learning models 13, each of the measures of benefit and the probability of compliance may be associated with a different weight, such different weight causing one to be more influential than the other. Such techniques may enable configuring which parameter (e.g., probability of compliance or measures of benefit) is more desirable to consider more heavily during generation of the treatment plan.

For example, as depicted, the patient profile display 130 presents “The following treatment plans are recommended for the patient based on one or more probabilities of the user complying with one or more exercise regimens and the respective measure of benefit the one or more exercises provide the user.” Then, the patient profile display 130 presents a first recommended treatment plan.

As depicted, treatment plan “1” indicates “Patient X should use treatment apparatus for 30 minutes a day for 4 days to achieve an increased range of motion of Y%. The exercises include a first exercise of pedaling the treatment apparatus for 30 minutes at a range of motion of Z% at 5 miles per hour, a second exercise of pedaling the treatment apparatus for 30 minutes at a range of motion of Y% at 10 miles per hour, etc. The first and second exercise satisfy a threshold compliance probability and/or a threshold measure of benefit which the exercise regimens provide to the user.” Accordingly, the treatment plan generated includes a first and second exercise, etc. that increase the range of motion of Y%. Further, in some embodiments, the exercises are indicated as satisfying a threshold compliance probability and/or a threshold measure of benefit which the exercise regimens provide to the user. Each of the exercises may specify any suitable parameter of the exercise and/or treatment apparatus 70 (e.g., duration of exercise, speed of motor of the treatment apparatus 70, range of motion setting of pedals, etc.). This specific example and all such examples elsewhere herein are not intended to limit in any way the generated treatment plan from recommending any suitable number and/or type of exercise.

Recommended treatment plan “2” may specify, based on a desired benefit, an indication of a probability of compliance, or some combination thereof, and different exercises for the user to perform.

As depicted, the patient profile display 130 may also present the excluded treatment plans 710. These types of treatment plans are shown to the assistant using the assistant interface 94 to alert the assistant not to recommend certain portions of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use treatment apparatus for longer than 30 minutes a day due to a heart condition.” Specifically, the excluded treatment plan points out a limitation of a treatment protocol where, due to a heart condition, Patient X should not exercise for more than 30 minutes a day. The excluded treatment plans may be based on treatment data (e.g., characteristics of the user, characteristics of the treatment apparatus 70, or the like).

The assistant may select the treatment plan for the patient on the overview display 120. For example, the assistant may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 708 for the patient.

In any event, the assistant may select the treatment plan for the patient to follow to achieve a desired result. The selected treatment plan may be transmitted to the patient interface 50 for presentation. The patient may view the selected treatment plan on the patient interface 50. In some embodiments, the assistant and the patient may discuss during the telemedicine session the details (e.g., treatment protocol using treatment apparatus 70, diet regimen, medication regimen, etc.) in real-time or in near real-time. In some embodiments, as discussed further with reference to method 1000 of FIG. 10 below, the server 30 may control, based on the selected treatment plan and during the telemedicine session, the treatment apparatus 70 as the user uses the treatment apparatus 70.

FIG. 8 shows an example embodiment of a method 800 for optimizing a treatment plan for a user to increase a probability of the user complying with the treatment plan according to the present disclosure. The method 800 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as is run on a general-purpose computer system or a dedicated machine), or a combination of both. The method 800 and/or each of its individual functions, routines, other methods, scripts, subroutines, or operations may be performed by one or more processors of a computing device (e.g., any component of FIG. 1 , such as server 30 executing the artificial intelligence engine 11). In certain implementations, the method 800 may be performed by a single processing thread. Alternatively, the method 800 may be performed by two or more processing threads, each thread implementing one or more individual functions or routines; or other methods, scripts, subroutines, or operations of the methods.

For simplicity of explanation, the method 800 is depicted and described as a series of operations. However, operations in accordance with this disclosure can occur in various orders and/or concurrently, and/or with other operations not presented and described herein. For example, the operations depicted in the method 800 may occur in combination with any other operation of any other method disclosed herein. Furthermore, not all illustrated operations may be required to implement the method 800 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method 800 could alternatively be represented as a series of interrelated states via a state diagram, a directed graph, a deterministic finite state automaton, a non-deterministic finite state automaton, a Markov diagram, or event diagrams.

At 802, the processing device may receive treatment data pertaining to a user (e.g., patient, volunteer, trainee, assistant, healthcare professional, instructor, etc.). The treatment data may include one or more characteristics (e.g., vital-sign or other measurements; performance; demographic; psychographic; geographic; diagnostic; measurement- or test-based; medically historic; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; arterial blood gas and/or oxygenation levels or percentages; psychographics; etc.) of the user. The treatment data may include one or more characteristics of the treatment apparatus 70. In some embodiments, the one or more characteristics of the treatment apparatus 70 may include a make (e.g., identity of entity that designed, manufactured, etc. the treatment apparatus 70) of the treatment apparatus 70, a model (e.g., model number or other identifier of the model) of the treatment apparatus 70, a year (e.g., year of manufacturing) of the treatment apparatus 70, operational parameters (e.g., motor temperature during operation; status of each sensor included in or associated with the treatment apparatus 70; the patient, or the environment; vibration measurements of the treatment apparatus 70 in operation; measurements of static and/or dynamic forces exerted on the treatment apparatus 70; etc.) of the treatment apparatus 70, settings (e.g., range of motion setting; speed setting; required pedal force setting; etc.) of the treatment apparatus 70, and the like. In some embodiments, the characteristics of the user and/or the characteristics of the treatment apparatus 70 may be tracked over time to obtain historical data pertaining to the characteristics of the user and/or the treatment apparatus 70. The foregoing embodiments shall also be deemed to include the use of any optional internal components or of any external components attachable to, but separate from the treatment apparatus itself. “Attachable” as used herein shall be physically, electronically, mechanically, virtually or in an augmented reality manner.

In some embodiments, when generating a treatment plan, the characteristics of the user and/or treatment apparatus 70 may be used. For example, certain exercises may be selected or excluded based on the characteristics of the user and/or treatment apparatus 70. For example, if the user has a heart condition, high intensity exercises may be excluded in a treatment plan. In another example, a characteristic of the treatment apparatus 70 may indicate the motor shudders, stalls or otherwise runs improperly at a certain number of revolutions per minute. In order to extend the lifetime of the treatment apparatus 70, the treatment plan may exclude exercises that include operating the motor at that certain revolutions per minute or at a prescribed manufacturing tolerance within those certain revolutions per minute.

At 804, the processing device may determine, via one or more trained machine learning models 13, a respective measure of benefit with which one or more exercises provide the user. In some embodiments, based on the treatment data, the processing device may execute the one or more trained machine learning models 13 to determine the respective measures of benefit. For example, the treatment data may include the characteristics of the user (e.g., heartrate, vital-sign, medical condition, injury, surgery, etc.), and the one or more trained machine learning models may receive the treatment data and output the respective measure of benefit with which one or more exercises provide the user. For example, if the user has a heart condition, a high intensity exercise may provide a negative benefit to the user, and thus, the trained machine learning model may output a negative measure of benefit for the high intensity exercise for the user. In another example, an exercise including pedaling at a certain range of motion may have a positive benefit for a user recovering from a certain surgery, and thus, the trained machine learning model may output a positive measure of benefit for the exercise regimen for the user.

At 806, the processing device may determine, via the one or more trained machine learning models 13, one or more probabilities associated with the user complying with the one or more exercise regimens. In some embodiments, the relationship between the one or more probabilities associated with the user complying with the one or more exercise regimens may be one to one, one to many, many to one, or many to many. The one or more probabilities of compliance may refer to a metric (e.g., value, percentage, number, indicator, probability, etc.) associated with a probability the user will comply with an exercise regimen. In some embodiments, the processing device may execute the one or more trained machine learning models 13 to determine the one or more probabilities based on (i) historical data pertaining to the user, another user, or both, (ii) received feedback from the user, another user, or both, (iii) received feedback from a treatment apparatus used by the user, or (iv) some combination thereof.

For example, historical data pertaining to the user may indicate a history of the user previously performing one or more of the exercises. In some instances, at a first time, the user may perform a first exercise to completion. At a second time, the user may terminate a second exercise prior to completion. Feedback data from the user and/or the treatment apparatus 70 may be obtained before, during, and after each exercise performed by the user. The trained machine learning model may use any combination of data (e.g., (i) historical data pertaining to the user, another user, or both, (ii) received feedback from the user, another user, or both, (iii) received feedback from a treatment apparatus used by the user) described above to learn a user compliance profile for each of the one or more exercises. The term “user compliance profile” may refer to a collection of histories of the user complying with the one or more exercise regimens. In some embodiments, the trained machine learning model may use the user compliance profile, among other data (e.g., characteristics of the treatment apparatus 70), to determine the one or more probabilities of the user complying with the one or more exercise regimens.

At 808, the processing device may transmit a treatment plan to a computing device. The computing device may be any suitable interface described herein. For example, the treatment plan may be transmitted to the assistant interface 94 for presentation to a healthcare professional, and/or to the patient interface 50 for presentation to the patient. The treatment plan may be generated based on the one or more probabilities and the respective measure of benefit the one or more exercises may provide to the user. In some embodiments, as described further below with reference to the method 1000 of FIG. 10 , while the user uses the treatment apparatus 70, the processing device may control, based on the treatment plan, the treatment apparatus 70.

In some embodiments, the processing device may generate, using at least a subset of the one or more exercises, the treatment plan for the user to perform, wherein such performance uses the treatment apparatus 70. The processing device may execute the one or more trained machine learning models 13 to generate the treatment plan based on the respective measure of the benefit the one or more exercises provide to the user, the one or more probabilities associated with the user complying with each of the one or more exercise regimens, or some combination thereof. For example, the one or more trained machine learning models 13 may receive the respective measure of the benefit the one or more exercises provide to the user, the one or more probabilities of the user associated with complying with each of the one or more exercise regimens, or some combination thereof as input and output the treatment plan.

In some embodiments, during generation of the treatment plan, the processing device may more heavily or less heavily weight the probability of the user complying than the respective measure of benefit the one or more exercise regimens provide to the user. During generation of the treatment plan, such a technique may enable one of the factors (e.g., the probability of the user complying or the respective measure of benefit the one or more exercise regimens provide to the user) to become more important than the other factor. For example, if desirable to select exercises that the user is more likely to comply with in a treatment plan, then the one or more probabilities of the user associated with complying with each of the one or more exercise regimens may receive a higher weight than one or more measures of exercise benefit factors. In another example, if desirable to obtain certain benefits provided by exercises, then the measure of benefit an exercise regimen provides to a user may receive a higher weight than the user compliance probability factor. The weight may be any suitable value, number, modifier, percentage, probability, etc.

In some embodiments, the processing device may generate the treatment plan using a non-parametric model, a parametric model, or a combination of both a non-parametric model and a parametric model. In statistics, a parametric model or finite-dimensional model refers to probability distributions that have a finite number of parameters. Non-parametric models include model structures not specified a priori but instead determined from data. In some embodiments, the processing device may generate the treatment plan using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other suitable mathematically-based prediction model. A Bayesian prediction model is used in statistical inference where Bayes’ theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayes’ theorem may describe the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, as additional data (e.g., user compliance data for certain exercises, characteristics of users, characteristics of treatment apparatuses, and the like) are obtained, the probabilities of compliance for users for performing exercise regimens may be continuously updated. The trained machine learning models 13 may use the Bayesian prediction model and, in preferred embodiments, continuously, constantly or frequently be re-trained with additional data obtained by the artificial intelligence engine 11 to update the probabilities of compliance, and/or the respective measure of benefit one or more exercises may provide to a user.

In some embodiments, the processing device may generate the treatment plan based on a set of factors. In some embodiments, the set of factors may include an amount, quality or other quality of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, an indication of an energy level of the user, or some combination thereof. For example, the set of factors may be included in the training data used to train and/or re-train the one or more machine learning models 13. For example, the set of factors may be labeled as corresponding to treatment data indicative of certain measures of benefit one or more exercises provide to the user, probabilities of the user complying with the one or more exercise regimens, or both.

FIG. 9 shows an example embodiment of a method 900 for generating a treatment plan based on a desired benefit, a desired pain level, an indication of a probability associated with complying with the particular exercise regimen, or some combination thereof, according to some embodiments. Method 900 includes operations performed by processors of a computing device (e.g., any component of FIG. 1 , such as server 30 executing the artificial intelligence engine 11). In some embodiments, one or more operations of the method 900 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 900 may be performed in the same or a similar manner as described above in regard to method 800. The operations of the method 900 may be performed in some combination with any of the operations of any of the methods described herein.

At 902, the processing device may receive user input pertaining to a desired benefit, a desired pain level, an indication of a probability associated with complying with a particular exercise regimen, or some combination thereof. The user input may be received from the patient interface 50. That is, in some embodiments, the patient interface 50 may present a display including various graphical elements that enable the user to enter a desired benefit of performing an exercise, a desired pain level (e.g., on a scale ranging from 1-10, 1 being the lowest pain level and 10 being the highest pain level), an indication of a probability associated with complying with the particular exercise regimen, or some combination thereof. For example, the user may indicate he or she would not comply with certain exercises (e.g., one-arm push-ups) included in an exercise regimen due to a lack of ability to perform the exercise and/or a lack of desire to perform the exercise. The patient interface 50 may transmit the user input to the processing device (e.g., of the server 30, assistant interface 94, or any suitable interface described herein).

At 904, the processing device may generate, using at least a subset of the one or more exercises, the treatment plan for the user to perform wherein the performance uses the treatment apparatus 70. The processing device may generate the treatment plan based on the user input including the desired benefit, the desired pain level, the indication of the probability associated with complying with the particular exercise regimen, or some combination thereof. For example, if the user selected a desired benefit of improved range of motion of flexion and extension of their knee, then the one or more trained machine learning models 13 may identify, based on treatment data pertaining to the user, exercises that provide the desired benefit. Those identified exercises may be further filtered based on the probabilities of user compliance with the exercise regimens. Accordingly, the one or more machine learning models 13 may be interconnected, such that the output of one or more trained machine learning models that perform function(s) (e.g., determine measures of benefit exercises provide to user) may be provided as input to one or more other trained machine learning models that perform other functions(s) (e.g., determine probabilities of the user complying with the one or more exercise regimens, generate the treatment plan based on the measures of benefit and/or the probabilities of the user complying, etc.).

FIG. 10 shows an example embodiment of a method 1000 for controlling, based on a treatment plan, a treatment apparatus 70 while a user uses the treatment apparatus 70, according to some embodiments. Method 1000 includes operations performed by processors of a computing device (e.g., any component of FIG. 1 , such as server 30 executing the artificial intelligence engine 11). In some embodiments, one or more operations of the method 1000 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 1000 may be performed in the same or a similar manner as described above in regard to method 800. The operations of the method 1000 may be performed in some combination with any of the operations of any of the methods described herein.

At 1002, the processing device may transmit, during a telemedicine or telehealth session, a recommendation pertaining to a treatment plan to a computing device (e.g., patient interface 50, assistant interface 94, or any suitable interface described herein). The recommendation may be presented on a display screen of the computing device in real-time (e.g., less than 2 seconds) in a portion of the display screen while another portion of the display screen presents video of a user (e.g., patient, healthcare professional, or any suitable user). The recommendation may also be presented on a display screen of the computing device in near time (e.g., preferably more than or equal to 2 seconds and less than or equal to 10 seconds) or with a suitable time delay necessary for the user of the display screen to be able to observe the display screen.

At 1004, the processing device may receive, from the computing device, a selection of the treatment plan. The user (e.g., patient, healthcare professional, assistant, etc.) may use any suitable input peripheral (e.g., mouse, keyboard, microphone, touchpad, etc.) to select the recommended treatment plan. The computing device may transmit the selection to the processing device of the server 30, which is configured to receive the selection. There may any suitable number of treatment plans presented on the display screen. Each of the treatment plans recommended may provide different results and the healthcare professional may consult, during the telemedicine session, with the user, to discuss which result the user desires. In some embodiments, the recommended treatment plans may only be presented on the computing device of the healthcare professional and not on the computing device of the user (patient interface 50). In some embodiments, the healthcare professional may choose an option presented on the assistant interface 94. The option may cause the treatment plans to be transmitted to the patient interface 50 for presentation. In this way, during the telemedicine session, the healthcare professional and the user may view the treatment plans at the same time in real-time or in near real-time, which may provide for an enhanced user experience for the patient and/or healthcare professional using the computing device.

After the selection of the treatment plan is received at the server 30, at 1006, while the user uses the treatment apparatus 70, the processing device may control, based on the selected treatment plan, the treatment apparatus 70. In some embodiments, controlling the treatment apparatus 70 may include the server 30 generating and transmitting control instructions to the treatment apparatus 70. In some embodiments, controlling the treatment apparatus 70 may include the server 30 generating and transmitting control instructions to the patient interface 50, and the patient interface 50 may transmit the control instructions to the treatment apparatus 70. The control instructions may cause an operating parameter (e.g., speed, orientation, required force, range of motion of pedals, etc.) to be dynamically changed according to the treatment plan (e.g., a range of motion may be changed to a certain setting based on the user achieving a certain range of motion for a certain period of time). The operating parameter may be dynamically changed while the patient uses the treatment apparatus 70 to perform an exercise. In some embodiments, during a telemedicine session between the patient interface 50 and the assistant interface 94, the operating parameter may be dynamically changed in real-time or near real-time.

FIG. 11 shows an example computer system 1100 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 1100 may include a computing device and correspond to the assistance interface 94, reporting interface 92, supervisory interface 90, clinician interface 20, server 30 (including the AI engine 11), patient interface 50, ambulatory sensor 82, goniometer 84, treatment apparatus 70, pressure sensor 86, or any suitable component of FIG. 1 , further the computer system 1100 may include the computing device 1200 of FIG. 12 . The computer system 1100 may be capable of executing instructions implementing the one or more machine learning models 13 of the artificial intelligence engine 11 of FIG. 1 . The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer-to-peer network. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

The computer system 1100 includes a processing device 1102, a main memory 1104 (e.g., read-only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 1106 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 1108, which communicate with each other via a bus 1110.

Processing device 1102 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 1102 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 1102 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 1102 is configured to execute instructions for performing any of the operations and steps discussed herein.

The computer system 1100 may further include a network interface device 1112. The computer system 1100 also may include a video display 1114 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 1116 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 1118 (e.g., a speaker). In one illustrative example, the video display 1114 and the input device(s) 1116 may be combined into a single component or device (e.g., an LCD touch screen).

The data storage device 1116 may include a computer-readable medium 1120 on which the instructions 1122 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 1122 may also reside, completely or at least partially, within the main memory 1104 and/or within the processing device 1102 during execution thereof by the computer system 1100. As such, the main memory 1104 and the processing device 1102 also constitute computer-readable media. The instructions 1122 may further be transmitted or received over a network via the network interface device 1112.

While the computer-readable storage medium 1120 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

FIG. 12 generally illustrates a perspective view of a person using the treatment apparatus 70, 100 of FIG. 2 , the patient interface 50, and a computing device 1200 according to the principles of the present disclosure. In some embodiments, the patient interface 50 may not be able to communicate via a network to establish a telemedicine session with the assistant interface 94. In such an instance the computing device 1200 may be used as a relay to receive cardiovascular data from one or more sensors attached to the user and transmit the cardiovascular data to the patient interface 50 (e.g., via Bluetooth), the server 30, and/or the assistant interface 94. The computing device 1200 may be communicatively coupled to the one or more sensors via a short range wireless protocol (e.g., Bluetooth). In some embodiments, the computing device 1200 may be connected to the assistant interface via a telemedicine session. Accordingly, the computing device 1200 may include a display configured to present video of the healthcare professional, to present instructional videos, to present treatment plans, etc. Further, the computing device 1200 may include a speaker configured to emit audio output, and a microphone configured to receive audio input (e.g., microphone).

In some embodiments, the computing device 1200 may be a smartphone capable of transmitting data via a cellular network and/or a wireless network. The computing device 1200 may include one or more memory devices storing instructions that, when executed, cause one or more processing devices to perform any of the methods described herein. The computing device 1200 may have the same or similar components as the computer system 1100 in FIG. 11 .

In some embodiments, the treatment apparatus 70 may include one or more stands configured to secure the computing device 1200 and/or the patient interface 50, such that the user can exercise hands-free.

In some embodiments, the computing device 1200 functions as a relay between the one or more sensors and a second computing device (e.g., assistant interface 94) of a healthcare professional, and a third computing device (e.g., patient interface 50) is attached to the treatment apparatus and presents, on the display, information pertaining to a treatment plan.

FIG. 13 generally illustrates a display 1300 of the computing device 1200, and the display presents a treatment plan 1302 designed to improve the user’s cardiovascular health according to the principles of the present disclosure.

As depicted, the display 1300 only includes sections for the user profile 130 and the video feed display 1308, including the self-video display 1310. During a telemedicine session, the user may operate the computing device 1200 in connection with the assistant interface 94. The computing device 1200 may present a video of the user in the self-video 1310, wherein the presentation of the video of the user is in a portion of the display 1300 that also presents a video from the healthcare professional in the video feed display 1308. Further, the video feed display 1308 may also include a graphical user interface (GUI) object 1306 (e.g., a button) that enables the user to share with the healthcare professional on the assistant interface 94 in real-time or near real-time during the telemedicine session the recommended treatment plans and/or excluded treatment plans. The user may select the GUI object 1306 to select one of the recommended treatment plans. As depicted, another portion of the display 1300 may include the user profile display 1300.

In FIG. 13 , the user profile display 1300 is presenting two example recommended treatment plans 1302 and one example excluded treatment plan 1304. As described herein, the treatment plans may be recommended based on a cardiovascular health issue of the user, a standardized measure comprising perceived exertion, cardiovascular data of the user, attribute data of the user, feedback data from the user, and the like. In some embodiments, the one or more trained machine learning models 13 may generate treatment plans that include exercises associated with increasing the user’s cardiovascular health by a certain threshold (e.g., any suitable percentage metric, value, percentage, number, indicator, probability, etc., which may be configurable). The trained machine learning models 13 may match the user to a certain cohort based on a probability of likelihood that the user fits that cohort. A treatment plan associated with that particular cohort may be prescribed for the user, in some embodiments.

For example, as depicted, the user profile display 1300 presents “Your characteristics match characteristics of users in Cohort A. The following treatment plans are recommended for you based on your characteristics and desired results.” Then, the user profile display 1300 presents a first recommended treatment plan. The treatment plans may include any suitable number of exercise sessions for a user. Each session may be associated with a different exertion level for the user to achieve or to maintain for a certain period of time. In some embodiments, more than one session may be associated with the same exertion level if having repeated sessions at the same exertion level are determined to enhance the user’s cardiovascular health. The exertion levels may change dynamically between the exercise sessions based on data (e.g., the cardiovascular health issue of the user, the standardized measure of perceived exertion, cardiovascular data, attribute data, etc.) that indicates whether the user’s cardiovascular health or some portion thereof is improving or deteriorating.

As depicted, treatment plan “1” indicates “Use treatment apparatus for 2 sessions a day for 5 days to improve cardiovascular health. In the first session, you should use the treatment apparatus at a speed of 5 miles per hour for 20 minutes to achieve a minimal desired exertion level. In the second session, you should use the treatment apparatus at a speed of 10 miles per hour 30 minutes a day for 4 days to achieve a high desired exertion level. The prescribed exercise includes pedaling in a circular motion profile.” This specific example and all such examples elsewhere herein are not intended to limit in any way the generated treatment plan from recommending any suitable number of exercises and/or type(s) of exercise.

As depicted, the patient profile display 1300 may also present the excluded treatment plans 1304. These types of treatment plans are shown to the user by using the computing device 1200 to alert the user not to perform certain treatment plans that could potentially harm the user’s cardiovascular health. For example, the excluded treatment plan could specify the following: “You should not use the treatment apparatus for longer than 40 minutes a day due to a cardiovascular health issue.” Specifically, in this example, the excluded treatment plan points out a limitation of a treatment protocol where, due to a cardiovascular health issue, the user should not exercise for more than 40 minutes a day. Excluded treatment plans may be based on results from other users having a cardiovascular heart issue when performing the excluded treatment plans, other users’ cardiovascular data, other users’ attributes, the standardized measure of perceived exertion, or some combination thereof.

The user may select which treatment plan to initiate. For example, the user may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 1302.

In some embodiments, the recommended treatment plans and excluded treatment plans may be presented on the display 120 of the assistant interface 94. The assistant may select the treatment plan for the user to follow to achieve a desired result. The selected treatment plan may be transmitted for presentation to the computing device 1200 and/or the patient interface 50. The patient may view the selected treatment plan on the computing device 1200 and/or patient interface 50. In some embodiments, the assistant and the patient may discuss the details (e.g., treatment protocol using treatment apparatus 70, diet regimen, medication regimen, etc.) during the telemedicine session in real-time or in near real-time. In some embodiments, as the user uses the treatment apparatus 70, as discussed further with reference to method 1000 of FIG. 10 above, the server 30 may control, based on the selected treatment plan and during the telemedicine session, the treatment apparatus 70.

FIG. 14 generally illustrates an example embodiment of a method 1400 for generating treatment plans, where such treatment plans may include sessions designed to enable a user, based on a standardized measure of perceived exertion, to achieve a desired exertion level according to the principles of the present disclosure. The method 1400 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software, or a combination of both. The method 1400 and/or each of their individual functions, subroutines, or operations may be performed by one or more processors of a computing device (e.g., the computing device 1200 of FIG. 12 and/or the patient interface 50 of FIG. 1 ) implementing the method 1400. The method 1400 may be implemented as computer instructions stored on a memory device and executable by the one or more processors. In certain implementations, the method 1400 may be performed by a single processing thread. Alternatively, the method 1400 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods.

At block 1402, the processing device may receive, at a computing device 1200, a first treatment plan designed to treat a cardiovascular health issue of a user. The cardiovascular heart issue may include diagnoses, diagnostic codes, symptoms, life consequences, comorbidities, risk factors to health, risk factors to life, etc. The cardiovascular heart issue may include heart surgery performed on the user, a heart transplant performed on the user, a heart arrhythmia of the user, an atrial fibrillation of the user, tachycardia, bradycardia, supraventricular tachycardia, congestive heart failure, heart valve disease, arteriosclerosis, atherosclerosis, pericardial disease, pericarditis, myocardial disease, myocarditis, cardiomyopathy, congenital heart disease, or some combination thereof.

The first treatment plan may include at least two exercise sessions that provide different exertion levels based at least on the cardiovascular health issue of the user. For example, if the user recently underwent heart surgery, then the user may be at high risk for a complication if their heart is overexerted. Accordingly, a first exercise session may begin with a very mild desired exertion level, and a second exercise session may slightly increase the exertion level. There may any suitable number of exercise sessions in an exercise protocol associated with the treatment plan. The number of sessions may depend on the cardiovascular health issue of the user. For example, the person who recently underwent heart surgery may be prescribed a higher number of sessions (e.g., 36) than the number of sessions prescribed in a treatment plan to a person with a less severe cardiovascular health issue. The first treatment plan may be presented on the display 1300 of the computing device 1200.

In some embodiments, the first treatment plan may also be generated by accounting for a standardized measure comprising perceived exertion, such as a metabolic equivalent of task (MET) value and/or the Borg Rating of Perceived Exertion (RPE). The MET value refers to an objective measure of a ratio of the rate at which a person expends energy relative to the mass of that person while performing a physical activity compared to a reference (resting rate). In other words, MET may refer to a ratio of work metabolic rate to resting metabolic rate. One MET may be defined as 1 kcal/kg/hour and approximately the energy cost of sitting quietly. Alternatively, and without limitation, one MET may be defined as oxygen uptake in ml/kg/min where one MET is equal to the oxygen cost of sitting quietly (e.g., 3.5 ml/kg/min). In this example, 1 MET is the rate of energy expenditure at rest. A 5 MET activity expends 5 times the energy used when compared to the energy used for by a body at rest. Cycling may be a 6 MET activity. If a user cycles for 30 minutes, then that is equivalent to 180 MET activity (i.e., 6 MET × 30 minutes). Attaining certain values of MET may be beneficial or detrimental for people having certain cardiovascular health issues.

A database may store a table including MET values for activities correlated with treatment plans, cardiovascular results of users having certain cardiovascular health issues, and/or cardiovascular data. The database may be continuously and/or continually updated as data is obtained from users performing treatment plans. The database may be used to train the one or more machine learning models such that improved treatment plans with exercises having certain MET values are selected. The improved treatment plans may result in faster cardiovascular health recovery time and/or a better cardiovascular health outcome. The improved treatment plans may result in reduced use of the treatment apparatus, computing device 1200, patient interface 50, server 30, and/or assistant interface 94. Accordingly, the disclosed techniques may reduce the resources (e.g., processing, memory, network) consumed by the treatment apparatus, computing device 1200, patient interface 50, server 30, and/or assistant interface 94, thereby providing a technical improvement. Further, wear and tear of the treatment apparatus, computing device 1200, patient interface 50, server 30, and/or assistant interface 94 may be reduced, thereby improving their lifespan.

The Borg RPE is a standardized way to measure physical activity intensity level. Perceived exertion refers to how hard a person feels like their body is working. The Borg RPE may be used to estimate a user’s actual heart rate during physical activity. The Borg RPE may be based on physical sensations a person experiences during physical activity, including increased heart rate, increased respiration or breathing rate, increased sweating, and/or muscle fatigue. The Borg rating scale may be from 6 (no exertion at all) to 20 (perceiving maximum exertion of effort). Similar to the MET table described above, the database may include a table that correlates the Borg values for activities with treatment plans, cardiovascular results of users having certain cardiovascular health issues, and/or cardiovascular data.

In some embodiments, the first treatment plan may be generated by one or more trained machine learning models. The machine learning models 13 may be trained by training engine 9. The one or more trained machine learning models may be trained using training data including labeled inputs of a standardized measure comprising perceived exertion, other users’ cardiovascular data, attribute data of the user, and/or other users’ cardiovascular health issues and a labeled output for a predicted treatment plan (e.g., the treatment plans may include details related to the number of exercise sessions, the exercises to perform at each session, the duration of the exercises, the exertion levels to maintain or achieve at each session, etc.). The attribute data may be received by the processing device and may include an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, of some combination thereof.

A mapping function may be used to map, using supervised learning, the labeled inputs to the labeled outputs, in some embodiments. In some embodiments, the machine learning models may be trained to output a probability that may be used to match to a treatment plan or match to a cohort of users that share characteristics similar to those of the user. If the user is matched to a cohort based on the probability, a treatment plan associated with that cohort may be prescribed to the user.

In some embodiments, the one or more machine learning models may include different layers of nodes that determine different outputs based on different data. For example, a first layer may determine, based on cardiovascular data of the user, a first probability of a predicted treatment plan. A second layer may receive the first probability and determine, based on the cardiovascular health issue of the user, a second probability of the predicted treatment plan. A third layer may receive the second probability and determine, based on the standardized measure of perceived exertion, a third probability of the predicted treatment plan. An activation function may combine the output from the third layer and output a final probability which may be used to prescribe the first treatment plan to the user.

In some embodiments, the first treatment plan may be designed and configured by a healthcare professional. In some embodiments, a hybrid approach may be used and the one or more machine learning models may recommend one or more treatment plans for the user and present them on the assistant interface 94. The healthcare professional may select one of the treatment plans, modify one of the treatment plans, or both, and the first treatment plan may be transmitted to the computing device 1200 and/or the patient interface 50.

At block 1404, while the user uses the treatment apparatus 70 to perform the first treatment plan for the user, the processing device may receive cardiovascular data from one or more sensors configured to measure the cardiovascular data associated with the user. In some embodiments, the treatment apparatus may include a cycling machine. The one or more sensors may include an electrocardiogram sensor, a pulse oximeter, a blood pressure sensor, a respiration rate sensor, a spirometry sensor, or some combination thereof. The electrocardiogram sensor may be a strap around the user’s chest, the pulse oximeter may be clip on the user’s finger, and the blood pressure sensor may be cuff on the user’s arm. Each of the sensors may be communicatively coupled with the computing device 1200 via Bluetooth or a similar near field communication protocol,. The cardiovascular data may include a cardiac output of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non-cardiovascular diagnosis of the user, a respiration rate of the user, spirometry data related to the user, or some combination thereof.

At block 1406, the processing device may transmit the cardiovascular data. In some embodiments, the cardiovascular data may be transmitted to the assistant interface 94 via the first network 34 and the second network 54. In some embodiments, the cardiovascular data may be transmitted to the server 30 via the second network 54. In some embodiments, cardiovascular data may be transmitted to the patient interface 50 (e.g., second computing device) which relays the cardiovascular data to the server 30 via the second network 58. In some embodiments, cardiovascular data may be transmitted to the patient interface 50 (e.g., second computing device) which relays the cardiovascular data to the assistant interface 94 (e.g., third computing device).

In some embodiments, one or more machine learning models 13 of the server 30 may be used to generate a second treatment plan. The second treatment plan may modify at least one of the exertion levels, and the modification may be based on a standardized measure of perceived exertion, the cardiovascular data, and the cardiovascular health issue of the user. In some embodiments, if the user is not able to meet or maintain the exertion level for a session, the one or more machine learning models 13 of the server 30 may modify the exertion level dynamically.

At block 1408, the processing device may receive the second treatment plan.

In some embodiments, the second treatment plan may include a modified parameter pertaining to the treatment apparatus 70. The modified parameter may include a resistance, a range of motion, a length of time, an angle of a component of the treatment apparatus, a speed, or some combination thereof. In some embodiments, while the user operates the treatment apparatus 70, the processing device may, based on the modified parameter in real-time or near real-time, cause the treatment apparatus 70 to be controlled .

In some embodiments, the one or more machine learning models may generate the second treatment plan by predicting exercises that will result in the desired exertion level for each session, and the one or more machine learning models may be trained using data pertaining to the standardized measure of perceived exertion, other users’ cardiovascular data, and other users’ cardiovascular health issues.

At block 1410, the processing device may present the second treatment plan on a display, such as the display 1300 of the computing device 1200.

In some embodiments, based on an operating parameter specified in the treatment plan, the second treatment plan, or both, the computing device 1200, the patient interface 50, the server 30, and/or the assistant interface 94 may send control instructions to control the treatment apparatus 70. The operating parameter may pertain to a speed of a motor of the treatment apparatus 70, a range of motion provided by one or more pedals of the treatment apparatus 70, an amount of resistance provided by the treatment apparatus 70, or the like.

FIG. 15 generally illustrates an example embodiment of a method 1500 for receiving input from a user and transmitting the feedback to be used to generate a new treatment plan according to the principles of the present disclosure. The method 1500 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software, or a combination of both. The method 1500 and/or each of their individual functions, subroutines, or operations may be performed by one or more processors of a computing device (e.g., the computing device 1200 of FIG. 12 and/or the patient interface 50 of FIG. 1 ) implementing the method 1500. The method 1500 may be implemented as computer instructions stored on a memory device and executable by the one or more processors. In certain implementations, the method 1500 may be performed by a single processing thread. Alternatively, the method 1500 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods.

At block 1502, while the user uses the treatment apparatus 70 to perform the first treatment plan for the user, the processing device may receive feedback from the user. The feedback may include input from a microphone, a touchscreen, a keyboard, a mouse, a touchpad, a wearable device, the computing device, or some combination thereof. In some embodiments, the feedback may pertain to whether or not the user is in pain, whether the exercise is too easy or too hard, whether or not to increase or decrease an operating parameter of the treatment apparatus 70, or some combination thereof.

At block 1504, the processing device may transmit the feedback to the server 30, wherein the one or more machine learning models uses the feedback to generate the second treatment plan.

FIG. 16 generally illustrates an example embodiment of a method 1600 for implementing a cardiac rehabilitation protocol by using artificial intelligence and a standardized measurement according to the principles of the present disclosure. The method 1600 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software, or a combination of both. The method 1600 and/or each of their individual functions, subroutines, or operations may be performed by one or more processing devices of a computing device (e.g., the computer system 1100 of FIG. 11 ) implementing the method 1600. The method 1600 may be implemented as computer instructions stored on a memory device and executable by the one or more processing devices. In certain implementations, the method 1600 may be performed by a single processing thread. Alternatively, the method 1600 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 1600. The system may include the treatment apparatus 70 (electromechanical machine) configured to be manipulated by a user while the user is performing a treatment plan, and an interface including a display configured to present information pertaining to the treatment plan. The system may include a processing device configured to execute instructions implemented the method 1600.

At block 1602, the processing device may determine a maximum target heart rate for a user using the electromechanical machine to perform the treatment plan. In some embodiments, the processing device may determine the maximum target heart rate by determining a heart rate reserve measure (HRRM) by subtracting from a maximum heart rate of the user a resting heart rate of the user.

At block 1604, the processing device may receive, via the interface (patient interface 50), an input pertaining to a perceived exertion level of the user. In some embodiments, the processing device may receive, via the interface, an input pertaining to a level of the user’s anxiety, depression, pain, difficulty in performing the treatment plan, or some combination thereof. In some embodiments, the processing device may receive, via the interface, an input pertaining to a physical activity readiness (PAR) score, and the processing device may determine, based on the PAR score, an initiation point at which the user is to begin the treatment plan. The treatment plan may pertain to cardiac rehabilitation, bariatric rehabilitation, cardio-oncologic rehabilitation, oncologic rehabilitation, pulmonary rehabilitation, or some combination thereof.

In some embodiments, the processing device may receive, from one or more sensors, performance data related to the user’s performance of the treatment plan. Based on the performance data, the input(s) received from the interface, or some combination thereof, the processing device may determine a state of the user.

At block 1606, based on the perceived exertion level and the maximum heart rate, the processing device may determine an amount of resistance for the electromechanical machine to provide via one or more pedals physically or communicatively coupled to the electromechanical machine. In some embodiments, the processing device may use one or more trained machine learning models that map one or more inputs to one or more outputs, wherein the mapping is to determine the amount of resistance the electromechanical machine is to provide via the one or more pedals. The one or more machine learning models 13 may be trained using a training dataset. The training dataset may include labeled inputs mapped to labeled outputs. The labeled inputs may pertain to one or more characteristics of one or more users (e.g., maximum target heart rates of users, perceived exertion levels of users during exercises using certain amounts of resistance, physiological data of users, health conditions of users, etc.) mapped to labeled outputs including amounts of resistance to provide by one or more pedals of an electromechanical machine.

At block 1608, while the user performs the treatment plan, the processing device may cause the electromechanical machine to provide the amount of resistance.

Further, in some embodiments, the processing device may transmit in real-time or near real-time one or more characteristic data of the user to a computing device used by a healthcare professional. The characteristic data may be transmitted to and presented on the computing device monitored by the healthcare professional. The characteristic data may include measurement data, performance data, and/or personal data pertaining to the user. For example, one or more wireless sensors may obtain the user’s heart rate, blood pressure, blood oxygen level, and the like at a certain frequency (e.g., every 5 minutes, every 2 minutes, every 30 seconds, etc.) and transmit those measurements to the computing device 1200 or the patient interface 50. The computing device 1200 and/or patient interface 50 may relay the measurements to the server 30, which may transmit the measurements for real-time display on the assistant interface 94.

In some embodiments, the processing device may receive, via one or more wireless sensors (e.g., blood pressure cuff, electrocardiogram wireless sensor, blood oxygen level sensor, etc.), one or more measurements including a blood pressure, a heart rate, a respiration rate, a blood oxygen level, or some combination thereof, in real-time or near real-time. In some embodiments, based on the one or more measurements, the processing device may determine whether the user’s heart rate is within a threshold relative to the maximum target heart rate. In some embodiments, if the one or more measurements exceed the threshold, the processing device may reduce the amount of resistance provided by the electromechanical machine. If the one or more measurements do not exceed the threshold, the processing device may maintain the amount of resistance provided by the electromechanical machine.

FIG. 17 generally illustrates an example embodiment of a method 1700 for using artificial intelligence and machine learning and telemedicine for invasive surgical-related treatment to determine a cardiac treatment plan that uses an electromechanical machine according to the principles of the present disclosure. As used herein, “invasive surgery” or “invasive surgical-related” describes surgeries or procedures, including interventional surgical treatments, that involve making an incision in the user or patient’s body and inserting instruments or other medical devices into it. The method 1700 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software, or a combination of both. The method 1700 and/or each of their individual functions, subroutines, or operations may be performed by one or more processing devices of a computing device (e.g., the computer system 1100 of FIG. 11 ) implementing the method 1700. The method 1700 may be implemented as computer instructions stored on a memory device and executable by the one or more processing devices. In certain implementations, the method 1700 may be performed by a single processing thread. Alternatively, the method 1700 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 1700. The system may include the treatment apparatus 70 (electromechanical machine) configured to be manipulated by a user while the user is performing a treatment plan, and an interface including a display configured to present information pertaining to the treatment plan. The system may include a processing device 1102 configured to execute instructions implemented the method 1700.

At block 1702, the processing device 1102 may receive, at a computing device (e.g., the computer system 1100 of FIG. 11 ), a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan may include at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels. In some embodiments, invasive surgery-related information pertaining to the user may be received from an application programming interface associated with an electronic medical records system. In some embodiments, the invasive surgical-related treatment health issue may include an oncologic health issue, another surgery-related health issue, or some combination thereof.

In some embodiments, the first treatment plan may be generated based on attribute data including an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a weight of the user information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, or some combination thereof.

At block 1704, while the user uses an electromechanical machine 70 to perform the first treatment plan for the user, the processing device 1102 may receive data from one or more sensors 76, 82, 84, 86 configured to measure the data associated with the invasive surgical-related health issue of the user. In some embodiments, the data may include other procedures performed on the user, an electronic medical record associated with the user, a weight of the user, a cardiac output of the user, a cardiac-related event (CRE) of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non-cardiovascular diagnosis of the user, a respiration rate of the user, spirometry data related to the user, a pulmonary diagnosis of the user, an oncologic diagnosis of the user, a bariatric diagnosis of the user, a pathological diagnosis related to a prostate gland or urogenital tract of the user, or some combination thereof. Further, the data may include information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.

At block 1706, the processing device 1102 may transmit the data. In some embodiments, one or more machine learning models 13 may be executed by the server 30 and the machine learning models 13 may be used to generate a second treatment plan based on the data and/or the invasive surgical-related health issues of users. The second treatment plan may modify at least one exertion level, and the modification may be based on a standardized measure including perceived exertion, the data, and the invasive surgical-related health issue of the user. In some embodiments, the standardized measure of perceived exertion may include a metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

In some embodiments, the one or more machine learning models 13 generate the second treatment plan by predicting exercises that will result in the desired exertion level for each session. The one or more machine learning models 13 may be trained using data pertaining to the standardized measure of perceived exertion, other users’ data, and other users’ invasive surgical-related health issues as input data, and other users’ exertion levels that led to desired results as output data. The input data and the output data may be labeled and mapped accordingly.

At block 1708, the processing device 1102 may receive the second treatment plan from the server 30. The processing device 1102 may implement at least a portion of the treatment plan to cause an operating parameter of the electromechanical machine 70 to be modified in accordance with the modified exertion level set in the second treatment plan. To that end, in some embodiments, the second treatment plan may include a modified parameter pertaining to the electromechanical machine 70. The modified parameter may include a resistance, a range of motion, a length of time, an angle of a component of the electromechanical machine 70, a speed, a velocity, an angular velocity, an acceleration, a torque, or some combination thereof. The processing device 1102 may, based on the modified parameter, control the electromechanical machine 70.

In some embodiments, transmitting the data may include transmitting the data to a second computing device that relays the invasive surgical-related health issue data to a third computing device that is associated with a healthcare professional.

FIG. 18 generally illustrates an embodiment of an enhanced patient display 1800 of the patient interface presenting real-time measurement information during a telemedicine session according to the principles of the present disclosure. As depicted, the enhanced patient display 1800 includes two graphical elements that represent two real-time or near real-time video feeds associated with the user and the healthcare professional (e.g., observer). Further, the enhanced patient display 1800 presents information pertaining to a treatment plan, such as a mode (e.g., Active Mode, Active-Assisted Mode, Passive Mode, and Resistive Mode), a session number (Session 1), an amount of time remaining in the session (e.g., 00:28:20), and a graphical element speedometer that represents the speed at which the user is pedaling, and further, the enhanced patient display 1800 provides instructions to the user.

In addition, the enhanced patient display 1800 may include one or more graphical elements that present real-time or near real-time measurement data to the user. For example, as depicted, the graphical elements present blood pressure data, blood oxygen data, and heart rate data to the user and the data may be streaming live as the user is performing the treatment plan using the electromechanical machine. The enhanced patient display 1800 may arrange the video feeds, the treatment plan information, and the measurement information in such a manner that improves the user’s experience using the computing device, thereby providing a technical improvement. For example, the layout of the display 1800 may be superior to other layouts, especially on a computing device with a reduced screen size, such as a tablet or smartphone.

In some embodiments, the invasive surgical-related health issue of the method in FIG. 17 , may include bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgeries, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof. This listing of possible invasive surgical-related health issues should be considered exemplary only and should not be considered as limiting. In more detail, the bariatric surgery may include surgeries such as, but not limited to gastric bypass, sleeve gastrectomy and adjustable gastric band. The breast surgery can include surgeries such as, but not limited to mastectomies, and breast augmentation and breast reduction surgeries. The colon and rectal surgery may include surgeries such as, but not limited to those related to anal cancer, anal condyloma, anal fissure, anal fistula, anal incontinence, anal sphincter repair, anorectal disease, colon cancer, diverticular disease, hemorrhoids, hereditary colon and rectal cancer, inflammatory bowel disease (IBS), polyps, rectal cancer, and rectal prolapse. The colon and rectal surgery can include surgeries such as, but not limited to thyroid surgery, parathryoidectomy, adrenalectomy. The gynecological surgery may include surgeries such as, but not limited to endometrial ablation, gynecologic cancer surgery, interventional radiology, tubal ligation, and uterine artery embolization (UAE). The head and neck surgery can include surgeries such as, but not limited to those related to nasal and sinus disorders, upper airway obstruction, throat, voice and swallowing problems, including gastroesophageal reflux therapy and tonsillectomies, diseases of the nerves in the ears, oral cavity, esophagus, head and face, benign and malignant tumors of the head and neck, facial deformities, including congenital (at birth) problems and issues resulting from accidents or disease, and structural problems related to hearing loss or impairment. The minimally invasive surgeries can, for example, involve the use of arthroscopic or laparoscopic devices. Such surgeries may also include remote-control manipulation of instruments, optionally with robotic devices such as Da Vinci surgical robots, with indirect observation of the surgical field through an endoscope or large scale display panel, and may be carried out through the skin or through a body cavity or anatomical opening. The neurosurgery may include surgeries such as, but not limited to those related to brain tumors, complicated cerebral aneurisms requiring skull-based approaches, microdiscectomies, callisectomies, spine surgeries, and thermal procedures. The orthopedic surgery can include surgeries such as, but not limited to Achilles tear surgery, meniscus repair surgery, open-knee surgery, shoulder surgery, and spinal surgery, any of the foregoing of which may involve the joint replacement of the hip, shoulder, elbow, knee or other joint. The outpatient surgery may include surgeries such as, but not limited to arthroscopy, breast biopsy, burn excision/debridement, Caesarean section, dental restoration, hysterectomy (abdominal or vaginal), knee/hip replacement, liver resection, lung resection, major abdominal procedure, major vascular surgery, mastectomy (radical), Mediport insertion or removal, prostate surgery, removal of hardware (plates and screws), tonsillectomy, vasectomy, and ventral hernia. The thoracic surgery can include surgeries such as, but not limited to lung airway surgery, pleural tumors surgery, pulmonary lobectomy and other lung cancer surgery. The urologic surgery may include surgeries such as, but not limited to those for prostate cancer, testicular cancer, kidney cancer and bladder cancer screening and surgery, surgical treatment for benign prostate enlargement, treatment of kidney stones and kidney cancer, bladder procedures for bleeding, incontinence, infection and cancers, and lithotripsy for bladder, kidney or gallbladder stones. The vascular surgery can include surgeries such as, but not limited to angioplasty and stents, aneurysm repair, carotid endarterectomy, lower extremity/limb salvage, and thrombolytic therapy.

In some embodiments, the one of more types of the invasive surgical-related health issue can be grouped into a plurality of surgery groups. For example, the treatment plan for users having some types of upper body extremity surgeries (e.g., to the arms or hands) includes certain exercises involving their lower body, as those users may perform such exercises without experiencing significant additional pain. Conversely, such users may not be able to engage in other exercises that involve their upper body. Thus, the one or more machine learning models 13 may generate the second treatment plan based on one or more of the plurality of surgery groups, including the invasive surgical-related health issue.

As mentioned above and with reference back to FIG. 4 , the user or patient can be queried to provide a pain level using the patient interface 50 (e.g., indicator showing “PAIN LEVEL 3”). Such a query can additionally or alternatively also be accomplished through patient display 1800. For instance, the user can communicate their pain level to the healthcare professional through the real-time or near real-time video feeds associated with the user and the healthcare professional. Accordingly, in some embodiments, the one or more machine learning models 13 may generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan. More specifically, users may experience different pain levels depending on the exact movements during the exercises of the treatment plan. For example, users that have had some type of abdominal surgery may experience high levels of pain doing certain movements of their torso or their legs. Thus, in some embodiments, the at least one pain level may include a plurality of pain levels each associated with one of a plurality of predetermined movements executed by the user.

As discussed above, prehabilitation can improve a patient’s speed of recovery, measure of quality of life, level of pain, etc. in upcoming surgeries or other procedures. FIG. 19 shows an example embodiment of a method 1900 that be used for such prehabilitation. The method 1900 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software, or a combination of both. The method 1900 and/or each of their individual functions, subroutines, methods (as the term is used in object-oriented programming), or operations may be performed by one or more processing devices of a computing device (e.g., the computer system 1100 of FIG. 11 ) implementing the method 1900. The method 1900 may be implemented as computer instructions stored on a memory device and executable by the one or more processing devices. In certain implementations, the method 1900 may be performed by a single processing thread. Alternatively, the method 1900 may be performed by two or more processing threads, each thread implementing one or more individual functions, methods (as the term is used in object-oriented programming), routines, subroutines, or operations of the methods (non object-oriented meaning).

In some embodiments, a system may be used to implement the method 1900. The system may include the treatment apparatus 70 (electromechanical machine) configured to be manipulated by a user while the user is performing a treatment plan, and an interface including a display configured to present information pertaining to the treatment plan. The system may include a processing device configured to execute instructions implemented the method 1900.

At block 1902, the processing device 1102 may receive a first treatment plan designed to prepare a user for a planned invasive surgery. The user may have a pre-surgery health before the planned invasive surgery. The first treatment plan comprises at least two exercise sessions that, based on the pre-surgery health of the user, enable the user to perform an exercise at different exertion levels. In some embodiments, the pre-surgery health pertaining to the user may be received from an application programming interface associated with an electronic medical records system.

At block 1904, while the user uses an electromechanical machine 70 to perform the first treatment plan for the user, the processing device 1102 may receive data from one or more sensors 76, 82, 84, 86 configured to measure the data associated with the pre-surgery health of the user before the planned invasive surgery. The electromechanical machine 70 is configured to be used by the user while performing the first treatment plan.

At block 1906, the processing device 1102 may transmit the data. In some embodiments, one or more machine learning models 13 may be executed by the server 30. The machine learning models 13 may be used to generate a second treatment plan based on the data and/or the pre-surgery health of users. The second treatment plan may modify at least one exertion level, and the modification may be based on a standardized measure including perceived exertion, the data, and the pre-surgery health of the user.

At block 1908, the processing device 1102 may receive the second treatment plan from the server 30. The processing device may implement at least a portion of the treatment plan to cause an operating parameter of the electromechanical machine to be modified in accordance with the modified exertion level set in the second treatment plan.

As with the invasive surgical-related health issue associated with the method of FIG. 17 above, the planned invasive surgery may comprise, without limitation, bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, outpatient surgery, orthopedic surgery, ophthalmological surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof. This listing of planned invasive surgeries should be considered exemplary only and should not be considered as limiting.

Clause 1. A computer-implemented system, comprising: an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan; an interface comprising a display configured to present information pertaining to the treatment plan; and a processing device configured to: receive, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels; while the user uses the electromechanical machine to perform the first treatment plan for the user, receive data from one or more sensors configured to measure the data associated with the invasive surgical-related health issue of the user; transmit the data, wherein one or more machine learning models are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure of perceived exertion, the data, and the invasive surgical-related health issue of the user; and receive the second treatment plan.

2. The computer-implemented system of any clause herein, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.

3. The computer-implemented system of any clause herein, wherein the invasive surgical-related health issue further comprises an oncologic health issue, another surgery-related health issue, or some combination thereof.

4. The computer-implemented system of any clause herein, wherein the second treatment plan comprises a modified parameter pertaining to the electromechanical machine, wherein the modified parameter comprises a resistance, a range of motion, a length of time, an angle of a component of the electromechanical machine, a speed, a velocity, an angular velocity, an acceleration, a torque, or some combination thereof, and the computer-implemented system further comprises:

-   controlling the electromechanical machine based on the modified     parameter.

5. The computer-implemented system of any clause herein, wherein the standardized measure of perceived exertion comprises a metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

6. The computer-implemented system of any clause herein, wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the at least one exertion level desired for each session, and the one or more machine learning models are trained using data pertaining to the standardized measure of perceived exertion, other users’ data, and other users’ invasive surgical-related health issues.

7. The computer-implemented system of any clause herein, wherein the first treatment plan is generated based on attribute data comprising an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, information pertaining to invasive surgical-related health issues of other users, or some combination thereof.

8. The computer-implemented system of any clause herein, wherein the transmitting the data further comprises transmitting the data to a second computing device that relays the data to a third computing device of a healthcare professional.

9. The computer-implemented system of any clause herein, wherein the data comprises other procedures performed on the user, an electronic medical record associated with the user, a weight of the user, a cardiac output of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non-cardiovascular diagnosis of the user, a pulmonary diagnosis of the user, an oncologic diagnosis of the user, a respiration rate of the user, spirometry data related to the user, or some combination thereof.

10. The computer-implemented system of any clause herein, wherein the invasive surgical-related health issue comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.

11. The computer-implemented system of any clause herein, wherein one of more types of the invasive surgical-related health issue are grouped into a plurality of surgery groups and the one or more machine learning models generate the second treatment plan based on one or more of the plurality of surgery groups including the invasive surgical-related health issue.

12. The computer-implemented system of any clause herein, wherein the one or more machine learning models generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan.

13. The computer-implemented system of any clause herein, wherein the at least one pain level includes a plurality of pain levels each associated with one of a plurality of predetermined movements executed by the user.

14. A computer-implemented method comprising: receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels; while the user uses an electromechanical machine to perform the first treatment plan for the user, receiving, at the computing device, data from one or more sensors configured to measure the data associated with the invasive surgical-related health issue of the user, wherein the electromechanical machine is configured to be used by the user while performing the first treatment plan; transmitting the data, wherein one or more machine learning models are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure of perceived exertion, the data, and the invasive surgical-related health issue of the user; and receiving the second treatment plan.

15. The computer-implemented method of any clause herein, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.

16. The computer-implemented method of any clause herein, wherein the invasive surgical-related health issue further comprises an oncologic health issue, another surgery-related health issue, or some combination thereof.

17. The computer-implemented method of any clause herein, wherein the second treatment plan comprises a modified parameter pertaining to the electromechanical machine, wherein the modified parameter comprises a resistance, a range of motion, a length of time, an angle of a component of the electromechanical machine, a speed, a velocity, an angular velocity, an acceleration, a torque, or some combination thereof, and the computer-implemented method further comprises:

-   controlling the electromechanical machine based on the modified     parameter.

18. The computer-implemented method of any clause herein, wherein the standardized measure of perceived exertion comprises a metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

19. The computer-implemented method of any clause herein, wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the at least one exertion level desired for each session, and the one or more machine learning models are trained using data pertaining to the standardized measure of perceived exertion, other users’ data, and other users’ invasive surgical-related health issues.

20. The computer-implemented method of any clause herein, wherein the first treatment plan is generated based on attribute data comprising an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, information pertaining to invasive surgical-related health issues of other users, or some combination thereof.

21. The computer-implemented method of any clause herein, wherein the transmitting the data further comprises transmitting the data to a second computing device that relays the data to a third computing device of a healthcare professional.

22. The computer-implemented method of any clause herein, wherein the data comprises other procedures performed on the user, an electronic medical record associated with the user, a weight of the user, a cardiac output of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non-cardiovascular diagnosis of the user, a pulmonary diagnosis of the user, an oncologic diagnosis of the user, a respiration rate of the user, spirometry data related to the user, or some combination thereof.

23. The computer-implemented method of any clause herein, wherein the invasive surgical-related health issue comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.

24. The computer-implemented method of any clause herein, further comprising using the one or more machine learning models to generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan.

25. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:

-   receive, at a computing device, a first treatment plan designed to     treat an invasive surgical-related health issue of a user, wherein     the first treatment plan comprises at least two exercise sessions     that, based on the invasive surgical-related health issue of the     user, enable the user to perform an exercise at different exertion     levels; while the user uses an electromechanical machine to perform     the first treatment plan for the user, receive data from one or more     sensors configured to measure the data associated with the invasive     surgical-related health issue of the user, wherein the     electromechanical machine is configured to be used by the user while     performing the first treatment plan; transmit the data, wherein one     or more machine learning models are used to generate a second     treatment plan, wherein the second treatment plan modifies at least     one exertion level, and the modification is based on a standardized     measure comprising perceived exertion, the data, and the invasive     surgical-related health issue of the user; and receive the second     treatment plan.

26. The computer-readable medium of any clause herein, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.

27. The computer-readable medium of any clause herein, wherein the invasive surgical-related health issue comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.

28. The computer-readable medium of any clause herein, wherein the instructions further cause the one or more machine learning models to generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan.

29. A computer-implemented method comprising: receiving, at a computing device, a first treatment plan designed to prepare a user for a planned invasive surgery, wherein the user has a pre-surgery health before the planned invasive surgery and the first treatment plan comprises at least two exercise sessions that, based on the pre-surgery health of the user, enable the user to perform an exercise at different exertion levels; while the user uses an electromechanical machine to perform the first treatment plan for the user, receiving, at the computing device, data from one or more sensors configured to measure the data associated with the pre-surgery health of the user before the planned invasive surgery, wherein the electromechanical machine is configured to be used by the user while performing the first treatment plan; transmitting the data, wherein one or more machine learning models are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, the planned invasive surgery, and the pre-surgery health of the user; and receiving the second treatment plan.

30. The computer-implemented method of any clause herein, wherein the planned invasive surgery comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.

The above discussion is meant to be illustrative of the principles and various embodiments of the present disclosure. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

The various aspects, embodiments, implementations, or features of the described embodiments can be used separately or in any combination. The embodiments disclosed herein are modular in nature and can be used in conjunction with or coupled to other embodiments.

Consistent with the above disclosure, the examples of assemblies enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples. 

What is claimed is:
 1. A computer-implemented system, comprising: an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan; an interface comprising a display configured to present information pertaining to the treatment plan; and a processing device configured to: receive, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels; while the user uses the electromechanical machine to perform the first treatment plan for the user, receive data from one or more sensors configured to measure the data associated with the invasive surgical-related health issue of the user; transmit the data, wherein one or more machine learning models are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure of perceived exertion, the data, and the invasive surgical-related health issue of the user; and receive the second treatment plan.
 2. The computer-implemented system of claim 1, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.
 3. The computer-implemented system of claim 1, wherein the invasive surgical-related health issue further comprises an oncologic health issue, another surgery-related health issue, or some combination thereof.
 4. The computer-implemented system of claim 1, wherein the second treatment plan comprises a modified parameter pertaining to the electromechanical machine, wherein the modified parameter comprises a resistance, a range of motion, a length of time, an angle of a component of the electromechanical machine, a speed, a velocity, an angular velocity, an acceleration, a torque, or some combination thereof, and the computer-implemented system further comprises: controlling the electromechanical machine based on the modified parameter.
 5. The computer-implemented system of claim 1, wherein the standardized measure of perceived exertion comprises a metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).
 6. The computer-implemented system of claim 1, wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the at least one exertion level desired for each session, and the one or more machine learning models are trained using data pertaining to the standardized measure of perceived exertion, other users’ data, and other users’ invasive surgical-related health issues.
 7. The computer-implemented system of claim 1, wherein the first treatment plan is generated based on attribute data comprising an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, information pertaining to invasive surgical-related health issues of other users, or some combination thereof.
 8. The computer-implemented system of claim 1, wherein the transmitting the data further comprises transmitting the data to a second computing device that relays the data to a third computing device of a healthcare professional.
 9. The computer-implemented system of claim 1, wherein the data comprises other procedures performed on the user, an electronic medical record associated with the user, a weight of the user, a cardiac output of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non-cardiovascular diagnosis of the user, a pulmonary diagnosis of the user, an oncologic diagnosis of the user, a respiration rate of the user, spirometry data related to the user, or some combination thereof.
 10. The computer-implemented system of claim 1, wherein the invasive surgical-related health issue comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.
 11. The computer-implemented system of claim 10, wherein one of more types of the invasive surgical-related health issue are grouped into a plurality of surgery groups and the one or more machine learning models generate the second treatment plan based on one or more of the plurality of surgery groups including the invasive surgical-related health issue.
 12. The computer-implemented system of claim 1, wherein the one or more machine learning models generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan.
 13. The computer-implemented system of claim 12, wherein the at least one pain level includes a plurality of pain levels each associated with one of a plurality of predetermined movements executed by the user.
 14. A computer-implemented method comprising: receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels; while the user uses an electromechanical machine to perform the first treatment plan for the user, receiving, at the computing device, data from one or more sensors configured to measure the data associated with the invasive surgical-related health issue of the user, wherein the electromechanical machine is configured to be used by the user while performing the first treatment plan; transmitting the data, wherein one or more machine learning models are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure of perceived exertion, the data, and the invasive surgical-related health issue of the user; and receiving the second treatment plan.
 15. The computer-implemented method of claim 14, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.
 16. The computer-implemented method of claim 14, wherein the invasive surgical-related health issue further comprises an oncologic health issue, another surgery-related health issue, or some combination thereof.
 17. The computer-implemented method of claim 14, wherein the second treatment plan comprises a modified parameter pertaining to the electromechanical machine, wherein the modified parameter comprises a resistance, a range of motion, a length of time, an angle of a component of the electromechanical machine, a speed, a velocity, an angular velocity, an acceleration, a torque, or some combination thereof, and the computer-implemented method further comprises: controlling the electromechanical machine based on the modified parameter.
 18. The computer-implemented method of claim 14, wherein the standardized measure of perceived exertion comprises a metabolic equivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).
 19. The computer-implemented method of claim 14, wherein the one or more machine learning models generate the second treatment plan by predicting exercises that will result in the at least one exertion level desired for each session, and the one or more machine learning models are trained using data pertaining to the standardized measure of perceived exertion, other users’ data, and other users’ invasive surgical-related health issues.
 20. The computer-implemented method of claim 14, wherein the first treatment plan is generated based on attribute data comprising an eating or drinking schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, information pertaining to a weight of the user, information pertaining to a height of the user, information pertaining to a body mass index (BMI) of the user, information pertaining to a family history of cardiovascular health issues of the user, information pertaining to comorbidities of the user, information pertaining to desired health outcomes of the user if the treatment plan is followed, information pertaining to predicted health outcomes of the user if the treatment plan is not followed, information pertaining to invasive surgical-related health issues of other users, or some combination thereof.
 21. The computer-implemented method of claim 14, wherein the transmitting the data further comprises transmitting the data to a second computing device that relays the data to a third computing device of a healthcare professional.
 22. The computer-implemented method of claim 14, wherein the data comprises other procedures performed on the user, an electronic medical record associated with the user, a weight of the user, a cardiac output of the user, a heartrate of the user, a heart rhythm of the user, a blood pressure of the user, a blood oxygen level of the user, a cardiovascular diagnosis of the user, a non-cardiovascular diagnosis of the user, a pulmonary diagnosis of the user, an oncologic diagnosis of the user, a respiration rate of the user, spirometry data related to the user, or some combination thereof.
 23. The computer-implemented method of claim 14, wherein the invasive surgical-related health issue comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.
 24. The computer-implemented method of claim 14, further comprising using the one or more machine learning models to generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan.
 25. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to: receive, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user, wherein the first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue of the user, enable the user to perform an exercise at different exertion levels; while the user uses an electromechanical machine to perform the first treatment plan for the user, receive data from one or more sensors configured to measure the data associated with the invasive surgical-related health issue of the user, wherein the electromechanical machine is configured to be used by the user while performing the first treatment plan; transmit the data, wherein one or more machine learning models are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, and the invasive surgical-related health issue of the user; and receive the second treatment plan.
 26. The computer-readable medium of claim 25, wherein the data comprises information pertaining to cardiac health of the user, oncologic health of the user, pulmonary health of the user, bariatric health of the user, rehabilitation from pathologies related to a prostate gland or urogenital tract, or some combination thereof.
 27. The computer-readable medium of claim 25, wherein the invasive surgical-related health issue comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof.
 28. The computer-readable medium of claim 25, wherein the instructions further cause the one or more machine learning models to generate the second treatment plan based on at least one pain level on a numerical pain scale reported by the user temporally before or after the exercise of the first treatment plan.
 29. A computer-implemented method comprising: receiving, at a computing device, a first treatment plan designed to prepare a user for a planned invasive surgery, wherein the user has a pre-surgery health before the planned invasive surgery and the first treatment plan comprises at least two exercise sessions that, based on the pre-surgery health of the user, enable the user to perform an exercise at different exertion levels; while the user uses an electromechanical machine to perform the first treatment plan for the user, receiving, at the computing device, data from one or more sensors configured to measure the data associated with the pre-surgery health of the user before the planned invasive surgery, wherein the electromechanical machine is configured to be used by the user while performing the first treatment plan; transmitting the data, wherein one or more machine learning models are used to generate a second treatment plan, wherein the second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, the planned invasive surgery, and the pre-surgery health of the user; and receiving the second treatment plan.
 30. The computer-implemented method of claim 29, wherein the planned invasive surgery comprises bariatric surgery, breast surgery, colon and rectal surgery, endocrine surgery, gynecological surgery, hand surgery, head and neck surgery, hernia surgery, minimally invasive surgery, neurosurgery, orthopedic surgery, ophthalmological surgery, outpatient surgery, pediatric surgery, plastic and reconstructive surgery, thoracic surgery, urologic surgery, vascular surgery, cardiovascular surgery, gastroenterological surgery, or some combination thereof. 